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Title: GitHub - PeterouZh/Deep_Generative_Models: A collection of papers I am interested in. · GitHub

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https://github.com/PeterouZh/Deep_Generative_Models#renderer
https://github.com/eth-ait/aitviewerhttps://github.com/eth-ait/aitviewer
https://github.com/mitsuba-renderer/mitsuba3https://github.com/mitsuba-renderer/mitsuba3
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https://github.com/PeterouZh/Deep_Generative_Models#project
mmgenerationhttps://github.com/open-mmlab/mmgeneration
inr-ganhttps://github.com/universome/inr-gan
ADAhttps://github.com/NVlabs/stylegan2-ada-pytorch
awesome-image-translationhttps://github.com/weihaox/awesome-image-translation
awesome-gan-inversionhttps://github.com/weihaox/awesome-gan-inversion
naver-webtoon-faceshttps://github.com/bryandlee/naver-webtoon-faces
GAN Experimentshttp://www.nathanshipley.com/gan/#gan-015-toonify-layer-blending
timmhttps://github.com/rwightman/pytorch-image-models
fun-with-computer-graphicshttps://github.com/zheng95z/fun-with-computer-graphics
https://github.com/PeterouZh/Deep_Generative_Models#face
StyleGAN-nadahttps://github.com/rinongal/StyleGAN-nada
RetrieveInStylehttps://github.com/mchong6/RetrieveInStyle
View_Neural_Talking_Head_Synthesishttps://github.com/zhanglonghao1992/One-Shot_Free-View_Neural_Talking_Head_Synthesis
Anime2Sketchhttps://github.com/Mukosame/Anime2Sketch
https://github.com/PeterouZh/Deep_Generative_Models#3d
face3dhttps://github.com/YadiraF/face3d
DECAhttps://github.com/YadiraF/DECA
https://github.com/PeterouZh/Deep_Generative_Models#tools
bokehhttps://github.com/bokeh/bokeh
face-parsing.PyTorchhttps://github.com/zllrunning/face-parsing.PyTorch
label-studiohttps://github.com/heartexlabs/label-studio
streamlit-drawable-canvashttps://github.com/andfanilo/streamlit-drawable-canvas
face-alignmenthttps://github.com/1adrianb/face-alignment
remove images backgroundhttps://github.com/danielgatis/rembg
https://github.com/PeterouZh/Deep_Generative_Models#gui
https://github.com/gradio-app/gradiohttps://github.com/gradio-app/gradio
https://github.com/PeterouZh/Deep_Generative_Models#stylegan
https://github.com/justinpinkney/awesome-pretrained-stylegan2https://github.com/justinpinkney/awesome-pretrained-stylegan2
https://github.com/justinpinkney/awesome-pretrained-stylegan3https://github.com/justinpinkney/awesome-pretrained-stylegan3
generative-evaluation-prdchttps://github.com/clovaai/generative-evaluation-prdc
https://github.com/PeterouZh/Deep_Generative_Models#style-transfer
style-transfer-pytorchhttps://github.com/crowsonkb/style-transfer-pytorch
Stylebank-exphttps://github.com/PeterouZh/Stylebank-exp
https://github.com/PeterouZh/Deep_Generative_Models#art
https://github.com/fogleman/primitivehttps://github.com/fogleman/primitive
https://github.com/PeterouZh/Deep_Generative_Models#anime
https://github.com/TachibanaYoshino/AnimeGANhttps://github.com/TachibanaYoshino/AnimeGAN
https://github.com/TachibanaYoshino/AnimeGANv2https://github.com/TachibanaYoshino/AnimeGANv2
https://github.com/PeterouZh/Deep_Generative_Models#toc
To be readhttps://github.com/PeterouZh/Deep_Generative_Models#to-be-read
Disentanglementhttps://github.com/PeterouZh/Deep_Generative_Models#disentanglement
Inversionhttps://github.com/PeterouZh/Deep_Generative_Models#inversion
Encoderhttps://github.com/PeterouZh/Deep_Generative_Models#encoder
Surveyhttps://github.com/PeterouZh/Deep_Generative_Models#survey
GANshttps://github.com/PeterouZh/Deep_Generative_Models#gans
Style transferhttps://github.com/PeterouZh/Deep_Generative_Models#style-transfer
Metrichttps://github.com/PeterouZh/Deep_Generative_Models#metric
Spectrumhttps://github.com/PeterouZh/Deep_Generative_Models#spectrum
Weakly Supervised Object Localizationhttps://github.com/PeterouZh/Deep_Generative_Models#weakly-supervised-object-localization
NeRFhttps://github.com/PeterouZh/Deep_Generative_Models#nerf
3Dhttps://github.com/PeterouZh/Deep_Generative_Models#3d
https://github.com/PeterouZh/Deep_Generative_Models#arxiv
Perceptual Gradient Networkshttp://arxiv.org/abs/2105.01957
InfinityGAN: Towards Infinite-Resolution Image Synthesishttp://arxiv.org/abs/2104.03963
Aliasing Is Your Ally: End-to-End Super-Resolution from Raw Image Burstshttp://arxiv.org/abs/2104.06191
StylePeople: A Generative Model of Fullbody Human Avatarshttp://arxiv.org/abs/2104.08363
Cross-Domain and Disentangled Face Manipulation with 3D Guidancehttp://arxiv.org/abs/2104.11228
On Buggy Resizing Libraries and Surprising Subtleties in FID Calculationhttp://arxiv.org/abs/2104.11222
FDA: Fourier Domain Adaptation for Semantic Segmentationhttp://arxiv.org/abs/2004.05498
githubhttps://github.com/YanchaoYang/FDA
StyleMapGAN: Exploiting Spatial Dimensions of Latent in GAN for Real-Time Image Editinghttp://arxiv.org/abs/2104.14754
Learning a Deep Reinforcement Learning Policy Over the Latent Space of a Pre-Trained GAN for Semantic Age Manipulationhttp://arxiv.org/abs/2011.00954
GANalyze: Toward Visual Definitions of Cognitive Image Propertieshttp://arxiv.org/abs/1906.10112
On the “Steerability” of Generative Adversarial Networkshttp://arxiv.org/abs/1907.07171
Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representationhttp://arxiv.org/abs/2104.11116
Unsupervised Image-to-Image Translation via Pre-Trained StyleGAN2 Networkhttp://arxiv.org/abs/2010.05713
githubhttps://github.com/HideUnderBush/UI2I_via_StyleGAN2
DatasetGAN: Efficient Labeled Data Factory with Minimal Human Efforthttp://arxiv.org/abs/2104.06490
Anycost GANs for Interactive Image Synthesis and Editinghttps://arxiv.org/abs/2103.03243v1
Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalizationhttp://arxiv.org/abs/2104.05833
Positional Encoding as Spatial Inductive Bias in GANshttp://arxiv.org/abs/2012.05217
An Empirical Study of the Effects of Sample-Mixing Methods for Efficient Training of Generative Adversarial Networkshttps://arxiv.org/abs/2104.03535v1
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networkshttps://ieeexplore.ieee.org/document/9150840/
githubhttps://github.com/haofanwang/Score-CAM
Image Demoireing with Learnable Bandpass Filtershttp://arxiv.org/abs/2004.00406
Unveiling the Potential of Structure Preserving for Weakly Supervised Object Localizationhttp://arxiv.org/abs/2103.04523
LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directionshttp://arxiv.org/abs/2104.00820
Generating Images with Sparse Representationshttp://arxiv.org/abs/2103.03841
PiCIE: Unsupervised Semantic Segmentation Using Invariance and Equivariance in Clusteringhttp://arxiv.org/abs/2103.17070
Dual Contrastive Loss and Attention for GANshttps://arxiv.org/abs/2103.16748v1
Unsupervised Disentanglement of Linear-Encoded Facial Semanticshttps://arxiv.org/abs/2103.16605v1
Emergence of Object Segmentation in Perturbed Generative Modelshttp://arxiv.org/abs/1905.12663
githubhttps://github.com/adambielski/perturbed-seg
Unsupervised Discovery of DisentangledManifolds in GANshttp://arxiv.org/abs/2011.11842
githubhttps://github.com/anvoynov/GANLatentDiscovery
StyleCLIP: Text-Driven Manipulation of StyleGAN Imageryhttp://arxiv.org/abs/2103.17249
githubhttps://github.com/orpatashnik/StyleCLIP
Few-Shot Semantic Image Synthesis Using StyleGAN Priorhttp://arxiv.org/abs/2103.14877
https://github.com/PeterouZh/Deep_Generative_Models#disentanglement
GANSpace: Discovering Interpretable GAN Controlshttp://arxiv.org/abs/2004.02546
GANSpacehttps://github.com/harskish/ganspace
Interpreting the Latent Space of GANs for Semantic Face Editinghttp://arxiv.org/abs/1907.10786
InterFaceGANhttps://github.com/genforce/interfacegan
Closed-Form Factorization of Latent Semantics in GANshttp://arxiv.org/abs/2007.06600
sefahttps://github.com/genforce/sefa
StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generationhttp://arxiv.org/abs/2011.12799
StyleSpacehttps://github.com/xrenaa/StyleSpace-pytorch
Unsupervised Image Transformation Learning via Generative Adversarial Networkshttp://arxiv.org/abs/2103.07751
githubhttps://github.com/genforce/trgan
Resolution Dependent GAN Interpolation for Controllable Image Synthesis Between Domainshttp://arxiv.org/abs/2010.05334
toonifyhttps://github.com/justinpinkney/toonify
WarpedGANSpace: Finding Non-Linear RBF Paths in GAN Latent Spacehttp://arxiv.org/abs/2109.13357
https://github.com/PeterouZh/Deep_Generative_Models#semantic-hierarchy
Semantic Hierarchy Emerges in Deep Generative Representations for Scene Synthesishttp://arxiv.org/abs/1911.09267
https://github.com/PeterouZh/Deep_Generative_Models#inversion
https://github.com/PeterouZh/Deep_Generative_Models#optimization
Image2StyleGAN++: How to Edit the Embedded Images?http://arxiv.org/abs/1911.11544
Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?http://arxiv.org/abs/1904.03189
Inverting The Generator Of A Generative Adversarial Networkhttp://arxiv.org/abs/1611.05644
Feature-Based Metrics for Exploring the Latent Space of Generative Modelshttps://openreview.net/forum?id=BJslDBkwG
Understanding Deep Image Representations by Inverting Themhttp://arxiv.org/abs/1412.0035
Dreaming to Distill: Data-Free Knowledge Transfer via DeepInversionhttp://arxiv.org/abs/1912.08795
DeepInversionhttps://github.com/NVlabs/DeepInversion
IMAGINE: Image Synthesis by Image-Guided Model Inversionhttp://arxiv.org/abs/2104.05895
Image Processing Using Multi-Code GAN Priorhttp://arxiv.org/abs/1912.07116
mGANpriorhttps://github.com/genforce/mganprior
Generative Visual Manipulation on the Natural Image Manifoldhttp://arxiv.org/abs/1609.03552
GAN Dissection: Visualizing and Understanding Generative Adversarial Networkshttp://arxiv.org/abs/1811.10597
GAN-Based Projector for Faster Recovery with Convergence Guarantees in Linear Inverse Problemshttp://arxiv.org/abs/1902.09698
Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative Modelshttp://openaccess.thecvf.com/content_CVPR_2020/html/Daras_Your_Local_GAN_Designing_Two_Dimensional_Local_Attention_Mechanisms_for_CVPR_2020_paper.html
Rewriting a Deep Generative Modelhttp://arxiv.org/abs/2007.15646
Transforming and Projecting Images into Class-Conditional Generative Networkshttp://arxiv.org/abs/2005.01703
StyleGAN2 Distillation for Feed-Forward Image Manipulationhttps://arxiv.org/abs/2003.03581v2
On the “Steerability” of Generative Adversarial Networkshttp://arxiv.org/abs/1907.07171
Unsupervised Discovery of DisentangledManifolds in GANshttp://arxiv.org/abs/2011.11842
PIE: Portrait Image Embedding for Semantic Controlhttp://arxiv.org/abs/2009.09485
GANSpace: Discovering Interpretable GAN Controlshttp://arxiv.org/abs/2004.02546
When and How Can Deep Generative Models Be Inverted?http://arxiv.org/abs/2006.15555
Style Intervention: How to Achieve Spatial Disentanglement with Style-Based Generators?http://arxiv.org/abs/2011.09699
StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generationhttp://arxiv.org/abs/2011.12799
Navigating the GAN Parameter Space for Semantic Image Editinghttp://arxiv.org/abs/2011.13786
Mask-Guided Discovery of Semantic Manifolds in Generative Modelshttp://arxiv.org/abs/2105.07273
masked-gan-manifoldhttps://github.com/bmolab/masked-gan-manifold
StyleFlow: Attribute-Conditioned Exploration of StyleGAN-Generated Images Using Conditional Continuous Normalizing Flowshttp://arxiv.org/abs/2008.02401
StyleFlowhttps://github.com/RameenAbdal/StyleFlow
Disentangled Face Attribute Editing via Instance-Aware Latent Space Searchhttp://arxiv.org/abs/2105.12660
Barbershop: GAN-Based Image Compositing Using Segmentation Maskshttp://arxiv.org/abs/2106.01505
Unsupervised Discovery of Interpretable Directions in the GAN Latent Spacehttp://arxiv.org/abs/2002.03754
GANLatentDiscoveryhttps://github.com/anvoynov/GANLatentDiscovery
Pivotal Tuning for Latent-Based Editing of Real Imageshttp://arxiv.org/abs/2106.05744
PTIhttps://github.com/danielroich/PTI
Editing in Style: Uncovering the Local Semantics of GANshttp://arxiv.org/abs/2004.14367
Retrieve in Style: Unsupervised Facial Feature Transfer and Retrievalhttp://arxiv.org/abs/2107.06256
RetrieveInStylehttps://github.com/mchong6/RetrieveInStyle
StyleCariGAN: Caricature Generation via StyleGAN Feature Map Modulationhttp://arxiv.org/abs/2107.04331
A Simple Baseline for StyleGAN Inversionhttp://arxiv.org/abs/2104.07661
From Continuity to Editability: Inverting GANs with Consecutive Imageshttp://arxiv.org/abs/2107.13812
AgileGAN: Stylizing Portraits by Inversion-Consistent Transfer Learninghttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
Talk-to-Edit: Fine-Grained Facial Editing via Dialoghttp://arxiv.org/abs/2109.04425
Talk-to-Edithttps://github.com/yumingj/Talk-to-Edit
Improved StyleGAN Embedding: Where Are the Good Latents?http://arxiv.org/abs/2012.09036
II2Shttps://github.com/ZPdesu/II2S
EditGAN: High-Precision Semantic Image Editinghttps://arxiv.org/abs/2111.03186v1
editGAN_releasehttps://github.com/nv-tlabs/editGAN_release
Grasping the Arrow of Time from the Singularity: Decoding Micromotion in Low-Dimensional Latent Spaces from StyleGANhttp://arxiv.org/abs/2204.12696
Spatially-Adaptive Multilayer Selection for GAN Inversion and Editinghttp://arxiv.org/abs/2206.08357
sam_inversionhttps://github.com/adobe-research/sam_inversion
Real Image Inversion via Segmentshttp://arxiv.org/abs/2110.06269
Chunkmogrifyhttps://github.com/futscdav/Chunkmogrify
https://github.com/PeterouZh/Deep_Generative_Models#encoder
GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolutionhttp://arxiv.org/abs/2012.00739
GLEANhttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
Swapping Autoencoder for Deep Image Manipulationhttp://arxiv.org/abs/2007.00653
githubhttps://github.com/rosinality/swapping-autoencoder-pytorch
In-Domain GAN Inversion for Real Image Editinghttp://arxiv.org/abs/2004.00049
ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinementhttp://arxiv.org/abs/2104.02699
ReStylehttps://github.com/yuval-alaluf/restyle-encoder
Interpreting the Latent Space of GANs for Semantic Face Editinghttp://arxiv.org/abs/1907.10786
Face Identity Disentanglement via Latent Space Mappinghttp://arxiv.org/abs/2005.07728
Collaborative Learning for Faster StyleGAN Embeddinghttp://arxiv.org/abs/2007.01758
Unsupervised Discovery of DisentangledManifolds in GANshttp://arxiv.org/abs/2011.11842
Generative Hierarchical Features from Synthesizing Imageshttp://arxiv.org/abs/2007.10379
One Shot Face Swapping on Megapixelshttp://arxiv.org/abs/2105.04932
GAN Prior Embedded Network for Blind Face Restoration in the Wildhttps://arxiv.org/abs/2105.06070v1
Adversarial Latent Autoencodershttp://openaccess.thecvf.com/content_CVPR_2020/html/Pidhorskyi_Adversarial_Latent_Autoencoders_CVPR_2020_paper.html
ALAEhttps://github.com/podgorskiy/ALAE
Encoding in Style: A StyleGAN Encoder for Image-to-Image Translationhttp://arxiv.org/abs/2008.00951
psphttps://github.com/eladrich/pixel2style2pixel
Designing an Encoder for StyleGAN Image Manipulationhttp://arxiv.org/abs/2102.02766
encoder4editinghttps://github.com/omertov/encoder4editing
A Latent Transformer for Disentangled and Identity-Preserving Face Editinghttp://arxiv.org/abs/2106.11895
ShapeEditer: A StyleGAN Encoder for Face Swappinghttp://arxiv.org/abs/2106.13984
Force-in-Domain GAN Inversionhttp://arxiv.org/abs/2107.06050
StyleFusion: A Generative Model for Disentangling Spatial Segmentshttp://arxiv.org/abs/2107.07437
Perceptually Validated Precise Local Editing for Facial Action Units with StyleGANhttp://arxiv.org/abs/2107.12143
StyleGAN2 Distillation for Feed-Forward Image Manipulationhttps://arxiv.org/abs/2003.03581v2
GAN Inversion for Out-of-Range Images with Geometric Transformationshttp://arxiv.org/abs/2108.08998
DyStyle: Dynamic Neural Network for Multi-Attribute-Conditioned Style Editinghttp://arxiv.org/abs/2109.10737
DyStylehttps://github.com/phycvgan/DyStyle
High-Fidelity GAN Inversion for Image Attribute Editinghttp://arxiv.org/abs/2109.06590
Few-Shot Knowledge Transfer for Fine-Grained Cartoon Face Generationhttp://arxiv.org/abs/2007.13332
HyperInverter: Improving StyleGAN Inversion via Hypernetworkhttp://arxiv.org/abs/2112.00719
HyperInverterhttps://github.com/VinAIResearch/HyperInverter
padinvhttps://github.com/EzioBy/padinv
https://github.com/PeterouZh/Deep_Generative_Models#hybrid-optimization
Generative Visual Manipulation on the Natural Image Manifoldhttp://arxiv.org/abs/1609.03552
Semantic Photo Manipulation with a Generative Image Priorhttps://arxiv.org/abs/2005.07727
Seeing What a GAN Cannot Generatehttp://arxiv.org/abs/1910.11626
In-Domain GAN Inversion for Real Image Editinghttp://arxiv.org/abs/2004.00049
https://github.com/PeterouZh/Deep_Generative_Models#without-optimization
Closed-Form Factorization of Latent Semantics in GANshttp://arxiv.org/abs/2007.06600
GAN “Steerability” without Optimizationhttp://arxiv.org/abs/2012.05328
Low-Rank Subspaces in GANshttp://arxiv.org/abs/2106.04488
LARGE: Latent-Based Regression through GAN Semanticshttp://arxiv.org/abs/2107.11186
Orthogonal Jacobian Regularization for Unsupervised Disentanglement in Image Generationhttp://arxiv.org/abs/2108.07668
Controllable and Compositional Generation with Latent-Space Energy-Based Modelshttp://arxiv.org/abs/2110.10873
LACEhttps://github.com/NVlabs/LACE
Do Generative Models Know Disentanglement? Contrastive Learning Is All You Needhttp://arxiv.org/abs/2102.10543
DisCohttps://github.com/xrenaa/DisCo
https://github.com/PeterouZh/Deep_Generative_Models#dgp
Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulationhttp://arxiv.org/abs/2003.13659
DGPhttps://github.com/XingangPan/deep-generative-prior
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Modelshttp://openaccess.thecvf.com/content_CVPR_2020/html/Menon_PULSE_Self-Supervised_Photo_Upsampling_via_Latent_Space_Exploration_of_Generative_CVPR_2020_paper.html
PULSEhttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolutionhttp://arxiv.org/abs/2012.00739
Unsupervised Portrait Shadow Removal via Generative Priorshttp://arxiv.org/abs/2108.03466
Towards Real-World Blind Face Restoration with Generative Facial Priorhttp://arxiv.org/abs/2101.04061
GFPGANhttps://github.com/TencentARC/GFPGAN
Towards Vivid and Diverse Image Colorization with Generative Color Priorhttp://arxiv.org/abs/2108.08826
Self-Validation: Early Stopping for Single-Instance Deep Generative Priorshttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
One-Shot Generative Domain Adaptationhttp://arxiv.org/abs/2111.09876
Time-Travel Rephotographyhttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
codehttps://github.com/Time-Travel-Rephotography/Time-Travel-Rephotography.github.io
https://github.com/PeterouZh/Deep_Generative_Models#cls
Contrastive Model Inversion for Data-Free Knowledge Distillationhttp://arxiv.org/abs/2105.08584
Generative Models as a Data Source for Multiview Representation Learninghttp://arxiv.org/abs/2106.05258
Inverting and Understanding Object Detectorshttp://arxiv.org/abs/2106.13933
Deep Neural Networks Are Surprisingly Reversible: A Baseline for Zero-Shot Inversionhttp://arxiv.org/abs/2107.06304
Ensembling with Deep Generative Viewshttp://arxiv.org/abs/2104.14551
https://github.com/PeterouZh/Deep_Generative_Models#change-pose-implicitly
On the “Steerability” of Generative Adversarial Networkshttp://arxiv.org/abs/1907.07171
Interpreting the Latent Space of GANs for Semantic Face Editinghttp://arxiv.org/abs/1907.10786
GANSpace: Discovering Interpretable GAN Controlshttp://arxiv.org/abs/2004.02546
GANSpacehttps://github.com/harskish/ganspace
Closed-Form Factorization of Latent Semantics in GANshttp://arxiv.org/abs/2007.06600
sefahttps://github.com/genforce/sefa
StyleGAN of All Trades: Image Manipulation with Only Pretrained StyleGANhttp://arxiv.org/abs/2111.01619
Using Latent Space Regression to Analyze and Leverage Compositionality in GANshttps://arxiv.org/abs/2103.10426v1
https://github.com/PeterouZh/Deep_Generative_Models#survey
GAN Inversion: A Surveyhttp://arxiv.org/abs/2101.05278
https://github.com/PeterouZh/Deep_Generative_Models#gans
https://github.com/PeterouZh/Deep_Generative_Models#neurips-2021
Rebooting ACGAN: Auxiliary Classifier GANs with Stable Traininghttp://arxiv.org/abs/2111.01118
https://github.com/PeterouZh/Deep_Generative_Models#theory
Towards a Better Global Loss Landscape of GANshttp://arxiv.org/abs/2011.04926
On the Benefit of Width for Neural Networks: Disappearance of Bad Basinshttp://arxiv.org/abs/1812.11039
https://github.com/PeterouZh/Deep_Generative_Models#regs
The Hessian Penalty: A Weak Prior for Unsupervised Disentanglementhttp://arxiv.org/abs/2008.10599
https://github.com/PeterouZh/Deep_Generative_Models#detection
Self-Supervised Object Detection via Generative Image Synthesishttp://arxiv.org/abs/2110.09848
https://github.com/PeterouZh/Deep_Generative_Models#stylegans
A Style-Based Generator Architecture for Generative Adversarial Networkshttp://arxiv.org/abs/1812.04948
Analyzing and Improving the Image Quality of StyleGANhttp://arxiv.org/abs/1912.04958
Training Generative Adversarial Networks with Limited Datahttp://arxiv.org/abs/2006.06676
Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Datahttps://openreview.net/forum?id=spjlJ4jeM_
Alias-Free Generative Adversarial Networkshttp://arxiv.org/abs/2106.12423
alias-free-ganhttps://github.com/NVlabs/alias-free-gan
rep2https://github.com/duskvirkus/alias-free-gan
Transforming the Latent Space of StyleGAN for Real Face Editinghttp://arxiv.org/abs/2105.14230
TransStyleGANhttps://github.com/AnonSubm2021/TransStyleGAN
MobileStyleGAN: A Lightweight Convolutional Neural Network for High-Fidelity Image Synthesishttp://arxiv.org/abs/2104.04767
MobileStyleGANhttps://github.com/bes-dev/MobileStyleGAN.pytorch
Few-Shot Image Generation via Cross-Domain Correspondencehttp://arxiv.org/abs/2104.06820
few-shot-gan-adaptationhttps://github.com/utkarshojha/few-shot-gan-adaptation
EigenGAN: Layer-Wise Eigen-Learning for GANshttp://arxiv.org/abs/2104.12476
EigenGANhttps://github.com/LynnHo/EigenGAN-Tensorflow
Toward Spatially Unbiased Generative Modelshttp://arxiv.org/abs/2108.01285
toward_spatial_unbiasedhttps://github.com/jychoi118/toward_spatial_unbiased
Interpreting Generative Adversarial Networks for Interactive Image Generationhttp://arxiv.org/abs/2108.04896
Explaining in Style: Training a GAN to Explain a Classifier in StyleSpacehttp://arxiv.org/abs/2104.13369
explaining-in-stylehttps://github.com/google/explaining-in-style
Projected GANs Converge Fasterhttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
projected_ganhttps://github.com/autonomousvision/projected_gan
Towards Faster and Stabilized GAN Training for High-Fidelity Few-Shot Image Synthesishttps://openreview.net/forum?id=1Fqg133qRaI
githubhttps://github.com/lucidrains/lightweight-gan
Ensembling Off-the-Shelf Models for GAN Traininghttp://arxiv.org/abs/2112.09130
vision-aided-ganhttps://github.com/nupurkmr9/vision-aided-gan
StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasetshttp://arxiv.org/abs/2202.00273
When, Why, and Which Pretrained GANs Are Useful?http://arxiv.org/abs/2202.08937
A U-Net Based Discriminator for Generative Adversarial Networkshttp://arxiv.org/abs/2002.12655
https://github.com/PeterouZh/Deep_Generative_Models#transformer
Compositional Transformers for Scene Generationhttp://arxiv.org/abs/2111.08960
GAN-Supervised Dense Visual Alignmenthttp://arxiv.org/abs/2112.05143
gangealinghttps://github.com/wpeebles/gangealing
Improved Transformer for High-Resolution GANshttp://arxiv.org/abs/2106.07631
MaskGIT: Masked Generative Image Transformerhttp://arxiv.org/abs/2202.04200
StyleSwin: Transformer-Based GAN for High-Resolution Image Generationhttp://arxiv.org/abs/2112.10762
https://github.com/PeterouZh/Deep_Generative_Models#singan
ExSinGAN: Learning an Explainable Generative Model from a Single Imagehttp://arxiv.org/abs/2105.07350
https://github.com/PeterouZh/Deep_Generative_Models#video-1
Diverse Generation from a Single Video Made Possiblehttp://arxiv.org/abs/2109.08591
https://github.com/PeterouZh/Deep_Generative_Models#gans-1
Differentiable Augmentation for Data-Efficient GAN Traininghttp://arxiv.org/abs/2006.10738
Sampling Generative Networkshttp://arxiv.org/abs/1609.04468
Combining Transformer Generators with Convolutional Discriminatorshttp://arxiv.org/abs/2105.10189
Improving Generation and Evaluation of Visual Stories via Semantic Consistencyhttp://arxiv.org/abs/2105.10026
TediGAN: Text-Guided Diverse Face Image Generation and Manipulationhttp://arxiv.org/abs/2012.03308
Data-Efficient Instance Generation from Instance Discriminationhttp://arxiv.org/abs/2106.04566
Styleformer: Transformer Based Generative Adversarial Networks with Style Vectorhttp://arxiv.org/abs/2106.07023
FBC-GAN: Diverse and Flexible Image Synthesis via Foreground-Background Compositionhttp://arxiv.org/abs/2107.03166
ViTGAN: Training GANs with Vision Transformershttp://arxiv.org/abs/2107.04589
Learning Efficient GANs for Image Translation via Differentiable Masks and Co-Attention Distillationhttp://arxiv.org/abs/2011.08382
CGANs with Auxiliary Discriminative Classifierhttp://arxiv.org/abs/2107.10060
A Good Image Generator Is What You Need for High-Resolution Video Synthesishttp://arxiv.org/abs/2104.15069
Dual Projection Generative Adversarial Networks for Conditional Image Generationhttp://arxiv.org/abs/2108.09016
Your GAN Is Secretly an Energy-Based Model and You Should Use Discriminator Driven Latent Samplinghttp://arxiv.org/abs/2003.06060
CGAN-DDLShttps://github.com/JHpark1677/CGAN-DDLS
Manifold-Preserved GANshttp://arxiv.org/abs/2109.08955
Latent Reweighting, an Almost Free Improvement for GANshttp://arxiv.org/abs/2110.09803
STRANSGAN: AN EMPIRICAL STUDY ON TRANS- FORMER IN GANShttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
Self-Supervised GANs with Label Augmentationhttp://arxiv.org/abs/2106.08601
Regularizing Generative Adversarial Networks under Limited Datahttp://arxiv.org/abs/2104.03310
githubhttps://github.com/PeterouZh/lecam-gan
https://github.com/PeterouZh/Deep_Generative_Models#cgans
Unbiased Auxiliary Classifier GANs with MINEhttp://arxiv.org/abs/2006.07567
Twin Auxiliary Classifiers GANhttp://arxiv.org/abs/1907.02690
https://github.com/PeterouZh/Deep_Generative_Models#finetune
githubhttps://github.com/bryandlee/FreezeG
Freeze the Discriminator: A Simple Baseline for Fine-Tuning GANshttp://arxiv.org/abs/2002.10964
FreezeDhttps://github.com/sangwoomo/FreezeD
Fine-Tuning StyleGAN2 For Cartoon Face Generationhttp://arxiv.org/abs/2106.12445
Cartoon-StyleGANhttps://github.com/happy-jihye/Cartoon-StyleGAN
Transferring GANs: Generating Images from Limited Datahttp://arxiv.org/abs/1805.01677
Image Generation From Small Datasets via Batch Statistics Adaptationhttp://arxiv.org/abs/1904.01774
MineGAN: Effective Knowledge Transfer From GANs to Target Domains With Few Imageshttp://openaccess.thecvf.com/content_CVPR_2020/html/Wang_MineGAN_Effective_Knowledge_Transfer_From_GANs_to_Target_Domains_With_CVPR_2020_paper.html
https://github.com/PeterouZh/Deep_Generative_Models#compression
GAN Compression: Efficient Architectures for Interactive Conditional GANshttp://openaccess.thecvf.com/content_CVPR_2020/html/Li_GAN_Compression_Efficient_Architectures_for_Interactive_Conditional_GANs_CVPR_2020_paper.html
Online Multi-Granularity Distillation for GAN Compressionhttp://arxiv.org/abs/2108.06908
Revisiting Discriminator in GAN Compression: A Generator-Discriminator Cooperative Compression Schemehttp://arxiv.org/abs/2110.14439
GCChttps://github.com/SJLeo/GCC
https://github.com/PeterouZh/Deep_Generative_Models#detection-fake
Robust Attentive Deep Neural Network for Exposing GAN-Generated Faceshttp://arxiv.org/abs/2109.02167
https://github.com/PeterouZh/Deep_Generative_Models#segmentation
Labels4Free: Unsupervised Segmentation Using StyleGANhttp://arxiv.org/abs/2103.14968
BigDatasetGAN: Synthesizing ImageNet with Pixel-Wise Annotationshttp://arxiv.org/abs/2201.04684
https://github.com/PeterouZh/Deep_Generative_Models#datasets
Gradient-Based Learning Applied to Document Recognitionhttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
Learning Multiple Layers of Features from Tiny Imageshttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
ImageNet: A Large-Scale Hierarchical Image Databasehttps://ieeexplore.ieee.org/document/5206848/
Learning Hybrid Image Templates (HIT) by Information Projectionhttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
AnimalFacehttps://vcla.stat.ucla.edu/people/zhangzhang-si/HiT/exp5.html
A Style-Based Generator Architecture for Generative Adversarial Networkshttp://arxiv.org/abs/1812.04948
FFHQhttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
StarGAN v2: Diverse Image Synthesis for Multiple Domainshttp://arxiv.org/abs/1912.01865
AFHQhttps://github.com/clovaai/stargan-v2/blob/master/README.md#animal-faces-hq-dataset-afhq
Automated Flower Classification over a Large Number of Classeshttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
102Flowershttps://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html
XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappingshttp://arxiv.org/abs/1711.05139
CartoonSethttps://google.github.io/cartoonset/
Anime Faces Sourced from Safebooru Resized to 256x256https://www.kaggle.com/scribbless/another-anime-face-dataset
AnimeFacehttps://www.kaggle.com/scribbless/another-anime-face-dataset/metadata
Facial Expressions of Manga (Japanese Comic) Character Faceshttps://www.kaggle.com/mertkkl/manga-facial-expressions
MangaExpressionshttps://www.kaggle.com/mertkkl/manga-facial-expressions
Open-Source Cartoon Datasethttps://www.kaggle.com/arnaud58/photo2cartoon/version/1?select=trainB
photo2cartoonhttps://www.kaggle.com/arnaud58/photo2cartoon/version/1?select=trainB
Simpsons Faces: A Lot of Images of Your Favourite Charactershttps://www.kaggle.com/kostastokis/simpsons-faces?select=cropped
SimpsonsFaceshttps://www.kaggle.com/kostastokis/simpsons-faces?select=cropped
Bitmoji Faceshttps://www.kaggle.com/mostafamozafari/bitmoji-faces
BitmojiFaceshttps://www.kaggle.com/mostafamozafari/bitmoji-faces
BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generationhttps://arxiv.org/abs/2110.11728v1
AAHQhttps://github.com/onion-liu/aahq-dataset
Fake It Till You Make It: Face Analysis in the Wild Using Synthetic Data Alonehttp://arxiv.org/abs/2109.15102
FaceSyntheticshttps://github.com/microsoft/FaceSynthetics
Seeing 3D Chairs: Exemplar Part-Based 2D-3D Alignment Using a Large Dataset of CAD Modelshttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
A Large-Scale Car Dataset for Fine-Grained Categorization and Verificationhttp://arxiv.org/abs/1506.08959
The ArtBench Dataset: Benchmarking Generative Models with Artworkshttps://github.com/liaopeiyuan/artbench
DwNet: Dense Warp-Based Network for Pose-Guided Human Video Generationhttp://arxiv.org/abs/1910.09139
Fashionhttps://github.com/ubc-vision/DwNet
MoCoGAN: Decomposing Motion and Content for Video Generationhttp://arxiv.org/abs/1707.04993
Text2Human: Text-Driven Controllable Human Image Generationhttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
DeepFashion-MultiModalhttps://github.com/yumingj/DeepFashion-MultiModal
https://github.com/PeterouZh/Deep_Generative_Models#alias-ref
Alias-Free Generative Adversarial Networkshttp://arxiv.org/abs/2106.12423
On Buggy Resizing Libraries and Surprising Subtleties in FID Calculationhttp://arxiv.org/abs/2104.11222
https://github.com/PeterouZh/Deep_Generative_Models#texture
https://github.com/carson-katri/dream-textureshttps://github.com/carson-katri/dream-textures
https://github.com/PeterouZh/Deep_Generative_Models#tiles
TileGAN: Synthesis of Large-Scale Non-Homogeneous Textureshttp://arxiv.org/abs/1904.12795
InsetGAN for Full-Body Image Generationhttp://arxiv.org/abs/2203.07293
Collaging Class-Specific GANs for Semantic Image Synthesishttp://arxiv.org/abs/2110.04281
https://github.com/PeterouZh/Deep_Generative_Models#gan-application
SC-FEGAN: Face Editing Generative Adversarial Network with User’s Sketch and Colorhttp://arxiv.org/abs/1902.06838
Semantic Text-to-Face GAN -ST^2FGhttp://arxiv.org/abs/2107.10756
CRD-CGAN: Category-Consistent and Relativistic Constraints for Diverse Text-to-Image Generationhttp://arxiv.org/abs/2107.13516
https://github.com/PeterouZh/Deep_Generative_Models#image-to-image-translation
Image-to-Image Translation with Conditional Adversarial Networkshttp://arxiv.org/abs/1611.07004
pix2pixhttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANshttp://arxiv.org/abs/1711.11585
pix2pix-HDhttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networkshttp://arxiv.org/abs/1703.10593
CycleGANhttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translationhttp://arxiv.org/abs/1711.09020
StarGAN v2: Diverse Image Synthesis for Multiple Domainshttp://arxiv.org/abs/1912.01865
Multimodal Unsupervised Image-to-Image Translationhttp://arxiv.org/abs/1804.04732
MUNIThttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Networkhttp://arxiv.org/abs/2105.09188
MixerGAN: An MLP-Based Architecture for Unpaired Image-to-Image Translationhttp://arxiv.org/abs/2105.14110
GANs N’ Roses: Stable, Controllable, Diverse Image to Image Translation (Works for Videos Too!)http://arxiv.org/abs/2106.06561
Sketch Your Own GANhttp://arxiv.org/abs/2108.02774
Contrastive Learning for Unpaired Image-to-Image Translationhttp://arxiv.org/abs/2007.15651
contrastive-unpaired-translationhttps://github.com/taesungp/contrastive-unpaired-translation
The Animation Transformer: Visual Correspondence via Segment Matchinghttp://arxiv.org/abs/2109.02614
Image Synthesis via Semantic Compositionhttp://arxiv.org/abs/2109.07053
You Only Need Adversarial Supervision for Semantic Image Synthesishttp://arxiv.org/abs/2012.04781
https://github.com/PeterouZh/Deep_Generative_Models#style-transfer-1
https://github.com/nrupatunga/L0-Smoothinghttps://github.com/nrupatunga/L0-Smoothing
Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalizationhttp://arxiv.org/abs/1703.06868
Texture Synthesis Using Convolutional Neural Networkshttp://arxiv.org/abs/1505.07376
A Neural Algorithm of Artistic Stylehttp://arxiv.org/abs/1508.06576
Image Style Transfer Using Convolutional Neural Networkshttps://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Gatys_Image_Style_Transfer_CVPR_2016_paper.html
Perceptual Losses for Real-Time Style Transfer and Super-Resolutionhttp://arxiv.org/abs/1603.08155
Texture Networks: Feed-Forward Synthesis of Textures and Stylized Imageshttp://arxiv.org/abs/1603.03417
Attention-Based Stylisation for Exemplar Image Colourisationhttp://arxiv.org/abs/2105.01705
StyleBank: An Explicit Representation for Neural Image Style Transferhttps://arxiv.org/abs/1703.09210v2
Stylebankhttps://github.com/jxcodetw/Stylebank
Rethinking and Improving the Robustness of Image Style Transferhttp://arxiv.org/abs/2104.05623
Paint Transformer: Feed Forward Neural Painting with Stroke Predictionhttp://arxiv.org/abs/2108.03798
AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transferhttp://arxiv.org/abs/2108.03647
ZiGAN: Fine-Grained Chinese Calligraphy Font Generation via a Few-Shot Style Transfer Approachhttp://arxiv.org/abs/2108.03596
Domain-Aware Universal Style Transferhttp://arxiv.org/abs/2108.04441
Aesthetics and Neural Network Image Representationshttp://arxiv.org/abs/2109.08103
Collaborative Distillation for Ultra-Resolution Universal Style Transferhttp://arxiv.org/abs/2003.08436
collaborative-distillationhttps://github.com/mingsun-tse/collaborative-distillation
Adaptive Convolutions for Structure-Aware Style Transferhttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
ada-conv-pytorchhttps://github.com/RElbers/ada-conv-pytorch
CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transferhttp://arxiv.org/abs/2207.04808
CCPLhttps://github.com/JarrentWu1031/CCPL
https://github.com/PeterouZh/Deep_Generative_Models#metric--perceptual-loss
The Unreasonable Effectiveness of Deep Features as a Perceptual Metrichttp://arxiv.org/abs/1801.03924
lpips-pytorchhttps://github.com/S-aiueo32/lpips-pytorch
Generating Images with Perceptual Similarity Metrics Based on Deep Networkshttp://arxiv.org/abs/1602.02644
Generic Perceptual Loss for Modeling Structured Output Dependencieshttp://arxiv.org/abs/2103.10571
Inverting Adversarially Robust Networks for Image Synthesishttp://arxiv.org/abs/2106.06927
Demystifying MMD GANshttp://arxiv.org/abs/1801.01401
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibriumhttp://arxiv.org/abs/1706.08500
Improved Techniques for Training GANshttp://papers.nips.cc/paper/6125-improved-techniques-for-training-gans.pdf
High-Fidelity Performance Metrics for Generative Models in PyTorchhttps://github.com/toshas/torch-fidelity
Reliable Fidelity and Diversity Metrics for Generative Modelshttp://arxiv.org/abs/2002.09797
generative-evaluation-prdchttps://github.com/clovaai/generative-evaluation-prdc
The Contextual Loss for Image Transformation with Non-Aligned Datahttp://arxiv.org/abs/1803.02077
contextualLosshttps://github.com/roimehrez/contextualLoss
Maintaining Natural Image Statistics with the Contextual Losshttp://arxiv.org/abs/1803.04626
https://github.com/PeterouZh/Deep_Generative_Models#spectrum
Reproducibility of "FDA: Fourier Domain Adaptation ForSemantic Segmentationhttp://arxiv.org/abs/2104.14749
A Closer Look at Fourier Spectrum Discrepancies for CNN-Generated Images Detectionhttp://arxiv.org/abs/2103.17195
https://github.com/PeterouZh/Deep_Generative_Models#weakly-supervised-object-localization
TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localizationhttp://arxiv.org/abs/2103.14862
Finding an Unsupervised Image Segmenter in Each of Your Deep Generative Modelshttp://arxiv.org/abs/2105.08127
Segmentation in Style: Unsupervised Semantic Image Segmentation with Stylegan and CLIPhttp://arxiv.org/abs/2107.12518
https://github.com/PeterouZh/Deep_Generative_Models#implicit-neural-representations
https://github.com/vsitzmann/awesome-implicit-representationshttps://github.com/vsitzmann/awesome-implicit-representations
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationhttp://arxiv.org/abs/1901.05103
Occupancy Networks: Learning 3D Reconstruction in Function Spacehttp://arxiv.org/abs/1812.03828
Neural Image Representations for Multi-Image Fusion and Layer Separationhttp://arxiv.org/abs/2108.01199
Learning Continuous Image Representation with Local Implicit Image Functionhttp://arxiv.org/abs/2012.09161
https://github.com/PeterouZh/Deep_Generative_Models#energy
How to Train Your Energy-Based Modelshttp://arxiv.org/abs/2101.03288
Your Classifier Is Secretly an Energy Based Model and You Should Treat It Like Onehttp://arxiv.org/abs/1912.03263
JEMhttps://github.com/wgrathwohl/JEM
Generative Visual Prompt: Unifying Distributional Control of Pre-Trained Generative Modelshttp://arxiv.org/abs/2209.06970
Generative-Visual-Prompthttps://github.com/ChenWu98/Generative-Visual-Prompt
https://github.com/PeterouZh/Deep_Generative_Models#flow
Variational Inference with Normalizing Flowshttp://arxiv.org/abs/1505.05770
Density Estimation Using Real NVPhttp://arxiv.org/abs/1605.08803
https://github.com/PeterouZh/Deep_Generative_Models#chatgpt
https://github.com/golfzert/chatgpt-chinese-prompt-hackhttps://github.com/golfzert/chatgpt-chinese-prompt-hack
https://github.com/rawandahmad698/PyChatGPThttps://github.com/rawandahmad698/PyChatGPT
https://github.com/PeterouZh/Deep_Generative_Models#diffusion
https://github.com/heejkoo/Awesome-Diffusion-Modelshttps://github.com/heejkoo/Awesome-Diffusion-Models
https://github.com/huggingface/diffusershttps://github.com/huggingface/diffusers
https://github.com/Jack000/glid-3-xlhttps://github.com/Jack000/glid-3-xl
https://github.com/SirWaffle/AIrtist-k-diffusion-wraphttps://github.com/SirWaffle/AIrtist-k-diffusion-wrap
https://github.com/altryne/awesome-ai-art-image-synthesishttps://github.com/altryne/awesome-ai-art-image-synthesis
https://github.com/YangLing0818/Diffusion-Models-Papers-Survey-Taxonomyhttps://github.com/YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy
https://github.com/Jack000/glid-3-xl-stablehttps://github.com/Jack000/glid-3-xl-stable
https://github.com/Stability-AI/stablediffusionhttps://github.com/Stability-AI/stablediffusion
https://github.com/PeterouZh/Deep_Generative_Models#generation
Understanding Diffusion Models: A Unified Perspectivehttp://arxiv.org/abs/2208.11970
Deep Unsupervised Learning Using Nonequilibrium Thermodynamicshttp://arxiv.org/abs/1503.03585
Generative Modeling by Estimating Gradients of the Data Distributionhttp://arxiv.org/abs/1907.05600
Denoising Diffusion Probabilistic Modelshttp://arxiv.org/abs/2006.11239
diffusionhttps://github.com/hojonathanho/diffusion
denoising-diffusion-pytorchhttps://github.com/lucidrains/denoising-diffusion-pytorch
Denoising Diffusion Implicit Modelshttp://arxiv.org/abs/2010.02502
Improved Denoising Diffusion Probabilistic Modelshttps://arxiv.org/abs/2102.09672v1
improved-diffusionhttps://github.com/openai/improved-diffusion
Score-Based Generative Modeling through Stochastic Differential Equationshttps://openreview.net/forum?id=PxTIG12RRHS
Elucidating the Design Space of Diffusion-Based Generative Modelshttp://arxiv.org/abs/2206.00364
k-diffusionhttps://github.com/crowsonkb/k-diffusion
DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Stepshttp://arxiv.org/abs/2206.00927
dpm-solverhttps://github.com/LuChengTHU/dpm-solver
SDEdit: Image Synthesis and Editing with Stochastic Differential Equationshttp://arxiv.org/abs/2108.01073
SDEdithttps://github.com/ermongroup/SDEdit
D2C: Diffusion-Denoising Models for Few-Shot Conditional Generationhttp://arxiv.org/abs/2106.06819
Label-Efficient Semantic Segmentation with Diffusion Modelshttps://arxiv.org/abs/2112.03126v1
ddpm-segmentationhttps://github.com/yandex-research/ddpm-segmentation
Analog Bits: Generating Discrete Data Using Diffusion Models with Self-Conditioninghttp://arxiv.org/abs/2208.04202
bit-diffusionhttps://github.com/lucidrains/bit-diffusion
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noisehttp://arxiv.org/abs/2208.09392
Cold-Diffusion-Modelshttps://github.com/arpitbansal297/Cold-Diffusion-Models
Diffusion-GAN: Training GANs with Diffusionhttp://arxiv.org/abs/2206.02262
Diffusion-GANhttps://github.com/Zhendong-Wang/Diffusion-GAN
Tackling the Generative Learning Trilemma with Denoising Diffusion GANshttp://arxiv.org/abs/2112.07804
denoising-diffusion-ganhttps://github.com/NVlabs/denoising-diffusion-gan
Score-Based Generative Modeling in Latent Spacehttp://arxiv.org/abs/2106.05931
LSGMhttps://github.com/NVlabs/LSGM
Compositional Visual Generation with Composable Diffusion Modelshttp://arxiv.org/abs/2206.01714
Composable-Diffusionhttps://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
Accelerating Score-Based Generative Models with Preconditioned Diffusion Samplinghttp://arxiv.org/abs/2207.02196
PDShttps://github.com/fudan-zvg/PDS
Diffusion Autoencoders: Toward a Meaningful and Decodable Representationhttps://openaccess.thecvf.com/content/CVPR2022/html/Preechakul_Diffusion_Autoencoders_Toward_a_Meaningful_and_Decodable_Representation_CVPR_2022_paper.html
diffaehttps://github.com/phizaz/diffae
Cascaded Diffusion Models for High Fidelity Image Generationhttp://arxiv.org/abs/2106.15282
https://github.com/PeterouZh/Deep_Generative_Models#inversion-1
ILVR: Conditioning Method for Denoising Diffusion Probabilistic Modelshttp://arxiv.org/abs/2108.02938
ilvr_admhttps://github.com/jychoi118/ilvr_adm
Diffusion Models Beat GANs on Image Synthesishttp://arxiv.org/abs/2105.05233
guided-diffusionhttps://github.com/openai/guided-diffusion
An Image Is Worth One Word: Personalizing Text-to-Image Generation Using Textual Inversionhttp://arxiv.org/abs/2208.01618
textual_inversionhttps://github.com/rinongal/textual_inversion
DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationhttps://arxiv.org/abs/2208.12242v1
dreamboothhttps://dreambooth.github.io/
Dreambooth-Stable-Diffusionhttps://github.com/XavierXiao/Dreambooth-Stable-Diffusion
DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulationhttp://arxiv.org/abs/2110.02711
DiffusionCLIPhttps://github.com/gwang-kim/DiffusionCLIP
https://github.com/PeterouZh/Deep_Generative_Models#text-to-image
https://github.com/GeeveGeorge/Stable-Craiyonhttps://github.com/GeeveGeorge/Stable-Craiyon
Cross-Modal Contrastive Learning for Text-to-Image Generationhttp://arxiv.org/abs/2101.04702
Zero-Shot Text-to-Image Generationhttp://arxiv.org/abs/2102.12092
VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidancehttp://arxiv.org/abs/2204.08583
Learning Transferable Visual Models From Natural Language Supervisionhttps://arxiv.org/abs/2103.00020v1
CLIPhttps://github.com/moein-shariatnia/OpenAI-CLIP
open_cliphttps://github.com/mlfoundations/open_clip
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelshttp://arxiv.org/abs/2112.10741
Hierarchical Text-Conditional Image Generation with CLIP Latentshttp://arxiv.org/abs/2204.06125
DALLE2-pytorchhttps://github.com/lucidrains/DALLE2-pytorch
Photorealistic Text-to-Image Diffusion Models with Deep Language Understandinghttps://arxiv.org/abs/2205.11487v1
imagen-pytorchhttps://github.com/lucidrains/imagen-pytorch
Imagen-pytorchhttps://github.com/cene555/Imagen-pytorch
partihttps://github.com/google-research/parti
CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformershttp://arxiv.org/abs/2204.14217
High-Resolution Image Synthesis with Latent Diffusion Modelshttp://arxiv.org/abs/2112.10752
stable-diffusionhttps://github.com/pesser/stable-diffusion
latent-diffusionhttps://github.com/CompVis/latent-diffusion
stable-diffusionhttps://github.com/CompVis/stable-diffusion
Prompt-to-Prompt Image Editing with Cross Attention Controlhttp://arxiv.org/abs/2208.01626
CrossAttentionControlhttps://github.com/bloc97/CrossAttentionControl
SINE: SINgle Image Editing with Text-to-Image Diffusion Modelshttp://arxiv.org/abs/2212.04489
SINEhttps://github.com/zhang-zx/SINE
https://github.com/PeterouZh/Deep_Generative_Models#image_to_image
Palette: Image-to-Image Diffusion Modelshttp://arxiv.org/abs/2111.05826
Palette-Image-to-Image-Diffusion-Modelshttps://github.com/Janspiry/Palette-Image-to-Image-Diffusion-Models
Image Super-Resolution via Iterative Refinementhttp://arxiv.org/abs/2104.07636
Image-Super-Resolution-via-Iterative-Refinementhttps://github.com/Janspiry/Image-Super-Resolution-via-Iterative-Refinement
https://github.com/PeterouZh/Deep_Generative_Models#3d-1
https://github.com/neverix/pixel-dreamfusionhttps://github.com/neverix/pixel-dreamfusion
RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generationhttp://arxiv.org/abs/2211.09869
Magic3D: High-Resolution Text-to-3D Content Creationhttp://arxiv.org/abs/2211.10440
https://github.com/PeterouZh/Deep_Generative_Models#detection-1
DiffusionInst: Diffusion Model for Instance Segmentationhttp://arxiv.org/abs/2212.02773
DiffusionInsthttps://github.com/chenhaoxing/DiffusionInst
https://github.com/PeterouZh/Deep_Generative_Models#3d--nerf
https://www.meshlab.net/https://www.meshlab.net/
Surface Light Fields for 3D Photographyhttps://doi.org/10.1145/344779.344925
NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collectionshttp://arxiv.org/abs/2008.02268
nerfwhttps://github.com/PeterouZh/nerf_pl/tree/nerfw
Modulated Periodic Activations for Generalizable Local Functional Representationshttp://arxiv.org/abs/2104.03960
Neural Volume Rendering: NeRF And Beyondhttp://arxiv.org/abs/2101.05204
awesome-NeRFhttps://github.com/yenchenlin/awesome-NeRF
Editing Conditional Radiance Fieldshttp://arxiv.org/abs/2105.06466
editnerfhttps://github.com/stevliu/editnerf
Recursive-NeRF: An Efficient and Dynamically Growing NeRFhttp://arxiv.org/abs/2105.09103
MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereohttp://arxiv.org/abs/2103.15595
mvsnerfhttps://github.com/apchenstu/mvsnerf
Depth-Supervised NeRF: Fewer Views and Faster Training for Freehttp://arxiv.org/abs/2107.02791
Rethinking Positional Encodinghttp://arxiv.org/abs/2107.02561
Nerfies: Deformable Neural Radiance Fieldshttps://arxiv.org/abs/2011.12948v4
nerfieshttps://github.com/google/nerfies
Self-Calibrating Neural Radiance Fieldshttp://arxiv.org/abs/2108.13826
Light Field Networks: Neural Scene Representations with Single-Evaluation Renderinghttp://arxiv.org/abs/2106.02634
https://github.com/PeterouZh/Deep_Generative_Models#sine
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainshttp://arxiv.org/abs/2006.10739
Implicit Neural Representations with Periodic Activation Functionshttp://arxiv.org/abs/2006.09661
Modulated Periodic Activations for Generalizable Local Functional Representationshttp://arxiv.org/abs/2104.03960
Learned Initializations for Optimizing Coordinate-Based Neural Representationshttp://arxiv.org/abs/2012.02189
nerf-metahttps://github.com/sanowar-raihan/nerf-meta
Seeing Implicit Neural Representations as Fourier Serieshttp://arxiv.org/abs/2109.00249
https://github.com/PeterouZh/Deep_Generative_Models#inr
Adversarial Generation of Continuous Imageshttp://arxiv.org/abs/2011.12026
inr-ganhttps://github.com/universome/inr-gan
Image Generators with Conditionally-Independent Pixel Synthesishttp://arxiv.org/abs/2011.13775
CIPShttps://github.com/saic-mdal/CIPS
A Structured Dictionary Perspective on Implicit Neural Representationshttp://arxiv.org/abs/2112.01917
https://github.com/PeterouZh/Deep_Generative_Models#3d--nerf-gans
https://mrtornado24.github.io/Next3D/https://mrtornado24.github.io/Next3D/
HoloGAN: Unsupervised Learning of 3D Representations from Natural Imageshttp://arxiv.org/abs/1904.01326
BlockGAN: Learning 3D Object-Aware Scene Representations from Unlabelled Imageshttp://arxiv.org/abs/2002.08988
GRAF: Generative Radiance Fields for 3D-Aware Image Synthesishttp://arxiv.org/abs/2007.02442
Pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesishttp://arxiv.org/abs/2012.00926
pi-GANhttps://github.com/marcoamonteiro/pi-GAN
GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fieldshttp://arxiv.org/abs/2011.12100
giraffehttps://github.com/autonomousvision/giraffe
GIRAFFE HD: A High-Resolution 3D-Aware Generative Modelhttp://arxiv.org/abs/2203.14954
StyleNeRF: A Style-Based 3D-Aware Generator for High-Resolution Image Synthesishttp://arxiv.org/abs/2110.08985
CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fieldshttp://arxiv.org/abs/2103.17269
GNeRF: GAN-Based Neural Radiance Field without Posed Camerahttp://arxiv.org/abs/2103.15606
gnerfhttps://github.com/MQ66/gnerf
Unconstrained Scene Generation with Locally Conditioned Radiance Fieldshttp://arxiv.org/abs/2104.00670
ml-gsnhttps://github.com/apple/ml-gsn
Learning Object-Compositional Neural Radiance Field for Editable Scene Renderinghttp://arxiv.org/abs/2109.01847
A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesishttp://arxiv.org/abs/2110.15678
Generative Occupancy Fields for 3D Surface-Aware Image Synthesishttp://arxiv.org/abs/2111.00969
Efficient Geometry-Aware 3D Generative Adversarial Networkshttp://arxiv.org/abs/2112.07945
eg3dhttps://github.com/NVlabs/eg3d
3D-Aware Image Synthesis via Learning Structural and Textural Representationshttp://arxiv.org/abs/2112.10759
VolumeGANhttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
GRAM: Generative Radiance Manifolds for 3D-Aware Image Generationhttp://arxiv.org/abs/2112.08867
GRAMhttps://github.com/microsoft/GRAM
CoordGAN: Self-Supervised Dense Correspondences Emerge from GANshttp://arxiv.org/abs/2203.16521
Disentangled3D: Learning a 3D Generative Model with Disentangled Geometry and Appearance from Monocular Imageshttp://arxiv.org/abs/2203.15926
Multi-View Consistent Generative Adversarial Networks for 3D-Aware Image Synthesishttp://arxiv.org/abs/2204.06307
MVCGANhttps://github.com/Xuanmeng-Zhang/MVCGAN
FENeRF: Face Editing in Neural Radiance Fieldshttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
FENeRFhttps://github.com/MrTornado24/FENeRF
IDE-3D: Interactive Disentangled Editing for High-Resolution 3D-Aware Portrait Synthesishttp://arxiv.org/abs/2205.15517
EpiGRAF: Rethinking Training of 3D GANshttp://arxiv.org/abs/2206.10535
epigrafhttps://github.com/universome/epigraf
https://github.com/rethinking-3d-gans/codehttps://github.com/rethinking-3d-gans/code
Generative Multiplane Images: Making a 2D GAN 3D-Awarehttp://arxiv.org/abs/2207.10642
ml-gmpihttps://github.com/apple/ml-gmpi
GAUDI: A Neural Architect for Immersive 3D Scene Generationhttp://arxiv.org/abs/2207.13751
ml-gaudihttps://github.com/apple/ml-gaudi
Deep Deformable 3D Caricatures with Learned Shape Controlhttps://dl.acm.org/doi/10.1145/3528233.3530748
DeepDeformable3DCaricatureshttps://github.com/ycjungSubhuman/DeepDeformable3DCaricatures
Injecting 3D Perception of Controllable NeRF-GAN into StyleGAN for Editable Portrait Image Synthesishttp://arxiv.org/abs/2207.10257
SURF-GANhttps://github.com/jgkwak95/SURF-GAN
Pix2NeRF: Unsupervised Conditional $\pi$-GAN for Single Image to Neural Radiance Fields Translationhttp://arxiv.org/abs/2202.13162
TT-GNeRFhttps://github.com/zhangqianhui/TT-GNeRF
https://github.com/PeterouZh/Deep_Generative_Models#diffusion-1
DiffuStereo: High Quality Human Reconstruction via Diffusion-Based Stereo Using Sparse Camerashttp://arxiv.org/abs/2207.08000
DiffuStereohttps://github.com/DSaurus/DiffuStereo
DiffRF: Rendering-Guided 3D Radiance Field Diffusionhttp://arxiv.org/abs/2212.01206
DiffRFhttps://sirwyver.github.io/DiffRF/
https://github.com/PeterouZh/Deep_Generative_Models#nerf-large-scene
Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughshttp://arxiv.org/abs/2112.10703
Block-NeRF: Scalable Large Scene Neural View Synthesishttp://arxiv.org/abs/2202.05263
BlockNeRFPytorchhttps://github.com/dvlab-research/BlockNeRFPytorch
IBRNet: Learning Multi-View Image-Based Renderinghttp://arxiv.org/abs/2102.13090
IBRNethttps://github.com/googleinterns/IBRNet
https://github.com/PeterouZh/Deep_Generative_Models#nerf
https://github.com/kakaobrain/NeRF-Factory/https://github.com/kakaobrain/NeRF-Factory/
https://github.com/openxrlab/xrnerfhttps://github.com/openxrlab/xrnerf
https://github.com/ActiveVisionLab/nerfmmhttps://github.com/ActiveVisionLab/nerfmm
https://github.com/ventusff/improved-nerfmmhttps://github.com/ventusff/improved-nerfmm
https://github.com/Kai-46/nerfplusplushttps://github.com/Kai-46/nerfplusplus
https://github.com/kwea123/nerf_plhttps://github.com/kwea123/nerf_pl
https://github.com/NVlabs/instant-ngphttps://github.com/NVlabs/instant-ngp
https://github.com/sxyu/nerfvishttps://github.com/sxyu/nerfvis
https://github.com/frozoul/4K-NeRFhttps://github.com/frozoul/4K-NeRF
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesishttp://arxiv.org/abs/2003.08934
nerf-pytorchhttps://github.com/yenchenlin/nerf-pytorch
NeRF--: Neural Radiance Fields Without Known Camera Parametershttp://arxiv.org/abs/2102.07064
nerfmmhttps://github.com/PeterouZh/nerfmm
improved-nerfmmhttps://github.com/ventusff/improved-nerfmm
NeRF++: Analyzing and Improving Neural Radiance Fieldshttp://arxiv.org/abs/2010.07492
nerfplusplushttps://github.com/Kai-46/nerfplusplus
FastNeRF: High-Fidelity Neural Rendering at 200FPShttp://arxiv.org/abs/2103.10380
KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPshttp://arxiv.org/abs/2103.13744
Plenoxels: Radiance Fields without Neural Networkshttp://arxiv.org/abs/2112.05131
svox2https://github.com/sxyu/svox2
Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughshttp://arxiv.org/abs/2112.10703
mega-nerfhttps://github.com/cmusatyalab/mega-nerf
Neural Sparse Voxel Fieldshttp://arxiv.org/abs/2007.11571
NSVFhttps://github.com/facebookresearch/NSVF
Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fieldshttp://arxiv.org/abs/2103.13415
Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fieldshttps://arxiv.org/abs/2111.12077v2
Neural Actor: Neural Free-View Synthesis of Human Actors with Pose Controlhttp://arxiv.org/abs/2106.02019
Instant Neural Graphics Primitives with a Multiresolution Hash Encodinghttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
instant-ngphttps://github.com/NVlabs/instant-ngp
Point-NeRF: Point-Based Neural Radiance Fieldshttp://arxiv.org/abs/2201.08845
pointnerfhttps://github.com/Xharlie/pointnerf
MoFaNeRF: Morphable Facial Neural Radiance Fieldhttp://arxiv.org/abs/2112.02308
Object-Centric Neural Scene Renderinghttps://arxiv.org/abs/2012.08503v1
Semantic View Synthesishttps://arxiv.org/abs/2008.10598v1
NeRS: Neural Reflectance Surfaces for Sparse-View 3D Reconstruction in the Wildhttps://arxiv.org/abs/2110.07604v3
MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesishttp://arxiv.org/abs/2103.14910
CodeNeRF: Disentangled Neural Radiance Fields for Object Categorieshttp://arxiv.org/abs/2109.01750
code-nerfhttps://github.com/wbjang/code-nerf
NeRF-SR: High-Quality Neural Radiance Fields Using Super-Samplinghttp://arxiv.org/abs/2112.01759
TensoRF: Tensorial Radiance Fieldshttp://arxiv.org/abs/2203.09517
TensoRFhttps://github.com/apchenstu/TensoRF
Sem2NeRF: Converting Single-View Semantic Masks to Neural Radiance Fieldshttp://arxiv.org/abs/2203.10821
CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fieldshttp://arxiv.org/abs/2112.05139
BARF: Bundle-Adjusting Neural Radiance Fieldshttp://arxiv.org/abs/2104.06405
Unified Implicit Neural Stylizationhttp://arxiv.org/abs/2204.01943
SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Imagehttp://arxiv.org/abs/2204.00928
NeRF-Editing: Geometry Editing of Neural Radiance Fieldshttp://arxiv.org/abs/2205.04978
NeRF-Editinghttps://github.com/IGLICT/NeRF-Editing
PixelNeRF: Neural Radiance Fields from One or Few Imageshttp://arxiv.org/abs/2012.02190
pixel-nerfhttps://github.com/sxyu/pixel-nerf
Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fieldshttps://arxiv.org/abs/2112.03907v1
refnerfhttps://dorverbin.github.io/refnerf/
https://github.com/PeterouZh/Deep_Generative_Models#3d-inversion
Unsupervised 3D Shape Completion through GAN Inversionhttp://arxiv.org/abs/2104.13366
3D GAN Inversion for Controllable Portrait Image Animationhttp://arxiv.org/abs/2203.13441
Pix2NeRF: Unsupervised Conditional $\pi$-GAN for Single Image to Neural Radiance Fields Translationhttp://arxiv.org/abs/2202.13162
inerfhttps://github.com/salykovaa/inerf
Shape, Pose, and Appearance from a Single Image via Bootstrapped Radiance Field Inversionhttp://arxiv.org/abs/2211.11674
nerf-from-imagehttps://github.com/google-research/nerf-from-image
https://github.com/PeterouZh/Deep_Generative_Models#dynamic
Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Sceneshttp://arxiv.org/abs/2011.13084
Neural-Scene-Flow-Fieldshttps://github.com/zl548/Neural-Scene-Flow-Fields.git
D-NeRF: Neural Radiance Fields for Dynamic Sceneshttp://arxiv.org/abs/2011.13961
D-NeRFhttps://github.com/albertpumarola/D-NeRF
Dynamic View Synthesis from Dynamic Monocular Videohttp://arxiv.org/abs/2105.06468
DynamicNeRFhttps://github.com/gaochen315/DynamicNeRF
HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fieldshttp://arxiv.org/abs/2106.13228
hypernerfhttps://github.com/google/hypernerf
Neural Radiance Flow for 4D View Synthesis and Video Processinghttps://arxiv.org/abs/2012.09790v2
Animatable Neural Implicit Surfaces for Creating Avatars from Videoshttp://arxiv.org/abs/2203.08133
https://github.com/PeterouZh/Deep_Generative_Models#voice
https://github.com/CorentinJ/Real-Time-Voice-Cloninghttps://github.com/CorentinJ/Real-Time-Voice-Cloning
https://github.com/PeterouZh/Deep_Generative_Models#hand
https://github.com/reyuwei/NIMBLE_modelhttps://github.com/reyuwei/NIMBLE_model
https://github.com/PeterouZh/Deep_Generative_Models#hair
https://github.com/clach/Realtime-Vulkan-Hairhttps://github.com/clach/Realtime-Vulkan-Hair
https://github.com/PeterouZh/Deep_Generative_Models#loose-garment
https://cape.is.tue.mpg.de/dataset.htmlhttps://cape.is.tue.mpg.de/dataset.html
Predicting Loose-Fitting Garment Deformations Using Bone-Driven Motion Networkshttp://arxiv.org/abs/2205.01355
VirtualBoneshttps://github.com/non-void/VirtualBones
TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Stylehttp://arxiv.org/abs/2003.04583
TailorNet_datasethttps://github.com/zycliao/TailorNet_dataset
Learning Implicit Templates for Point-Based Clothed Human Modelinghttps://arxiv.org/abs/2207.06955v1
3D Clothed Human Reconstruction in the Wildhttps://arxiv.org/abs/2207.10053v1
ClothWild_RELEASEhttps://github.com/hygenie1228/ClothWild_RELEASE
TightCap: 3D Human Shape Capture with Clothing Tightness Fieldhttp://arxiv.org/abs/1904.02601
TightCaphttps://github.com/ChenFengYe/TightCap
ARCH: Animatable Reconstruction of Clothed Humanshttp://arxiv.org/abs/2004.04572
ARCHhttps://github.com/Tessantess/ARCH
https://github.com/PeterouZh/Deep_Generative_Models#rigging
neural-blend-shapeshttps://github.com/PeizhuoLi/neural-blend-shapes
https://github.com/PeterouZh/Deep_Generative_Models#anime-body
Collaborative Neural Rendering Using Anime Character Sheetshttp://arxiv.org/abs/2207.05378
CoNRhttps://github.com/megvii-research/CoNR
https://github.com/PeterouZh/Deep_Generative_Models#body
https://github.com/3DFaceBody/awesome-3dbody-papershttps://github.com/3DFaceBody/awesome-3dbody-papers
https://github.com/openMVG/awesome_3DReconstruction_listhttps://github.com/openMVG/awesome_3DReconstruction_list
https://github.com/ytrock/THuman2.0-Datasethttps://github.com/ytrock/THuman2.0-Dataset
https://github.com/Danial-Kord/DigiHumanhttps://github.com/Danial-Kord/DigiHuman
https://github.com/zhaofuq/Instant-NSRhttps://github.com/zhaofuq/Instant-NSR
SMPL: A Skinned Multi-Person Linear Modelhttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
Expressive Body Capture: 3D Hands, Face, and Body from a Single Imagehttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
AMASS: Archive of Motion Capture as Surface Shapeshttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
AMASShttps://amass.is.tue.mpg.de/index.html
SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapeshttp://arxiv.org/abs/2104.03953
Animatable Neural Radiance Fields for Modeling Dynamic Human Bodieshttp://arxiv.org/abs/2105.02872
animatable_nerfhttps://github.com/zju3dv/animatable_nerf
Neural Actor: Neural Free-View Synthesis of Human Actors with Pose Controlhttp://arxiv.org/abs/2106.02019
Animatable Neural Radiance Fields from Monocular RGB Videoshttp://arxiv.org/abs/2106.13629
Anim-NeRFhttps://github.com/JanaldoChen/Anim-NeRF
VIBE: Video Inference for Human Body Pose and Shape Estimationhttp://arxiv.org/abs/1912.05656
VIBEhttps://github.com/mkocabas/VIBE
A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Posehttp://arxiv.org/abs/2102.06199
A-NeRFhttps://github.com/LemonATsu/A-NeRF
HumanNeRF: Free-Viewpoint Rendering of Moving People from Monocular Videohttp://arxiv.org/abs/2201.04127
humannerfhttps://github.com/chungyiweng/humannerf
The Power of Points for Modeling Humans in Clothinghttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
POPhttps://github.com/qianlim/POP
Neural Point-Based Shape Modeling of Humans in Challenging Clothinghttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
SkiRThttps://github.com/qianlim/SkiRT
StylePeople: A Generative Model of Fullbody Human Avatarshttp://arxiv.org/abs/2104.08363
NPMs: Neural Parametric Models for 3D Deformable Shapeshttp://arxiv.org/abs/2104.00702
ICON: Implicit Clothed Humans Obtained from Normalshttp://arxiv.org/abs/2112.09127
ICONhttps://github.com/YuliangXiu/ICON
GDNA: Towards Generative Detailed Neural Avatarshttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
SCANimate: Weakly Supervised Learning of Skinned Clothed Avatar Networkshttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
NeuralAnnot: Neural Annotator for 3D Human Mesh Training Setshttp://arxiv.org/abs/2011.11232
PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loophttp://arxiv.org/abs/2103.16507
Structured Local Radiance Fields for Human Avatar Modelinghttp://arxiv.org/abs/2203.14478
SelfRecon: Self Reconstruction Your Digital Avatar from Monocular Videohttp://arxiv.org/abs/2201.12792
SelfReconhttps://jby1993.github.io/SelfRecon/
arahhttps://github.com/taconite/arah-release
Neural Actor: Neural Free-View Synthesis of Human Actors with Pose Controlhttp://arxiv.org/abs/2106.02019
Neural_Actor_Main_Codehttps://github.com/lingjie0206/Neural_Actor_Main_Code
Generalizable Neural Performer: Learning Robust Radiance Fields for Human Novel View Synthesishttp://arxiv.org/abs/2204.11798
gnrhttps://github.com/generalizable-neural-performer/gnr
NeuMan: Neural Human Radiance Field from a Single Videohttps://arxiv.org/abs/2203.12575v1
ml-neumanhttps://github.com/apple/ml-neuman
Surface-Aligned Neural Radiance Fields for Controllable 3D Human Synthesishttp://arxiv.org/abs/2201.01683
surface-aligned-nerfhttps://github.com/pfnet-research/surface-aligned-nerf
LoRD: Local 4D Implicit Representation for High-Fidelity Dynamic Human Modelinghttp://arxiv.org/abs/2208.08622
LoRDhttps://github.com/BoyanJIANG/LoRD
TAVA: Template-Free Animatable Volumetric Actorshttp://arxiv.org/abs/2206.08929
tavahttps://github.com/facebookresearch/tava
Fast-SNARF: A Fast Deformer for Articulated Neural Fieldshttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
fast-snarfhttps://github.com/xuchen-ethz/fast-snarf
InstantAvatar: Learning Avatars from Monocular Video in 60 Secondshttp://arxiv.org/abs/2212.10550
InstantAvatarhttps://tijiang13.github.io/InstantAvatar/
https://github.com/PeterouZh/Deep_Generative_Models#body-generation
https://github.com/justimyhxu/awesome-3D-generationhttps://github.com/justimyhxu/awesome-3D-generation
DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotationshttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
DeepFashionhttps://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html
Text2Human: Text-Driven Controllable Human Image Generationhttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
Text2Humanhttps://github.com/yumingj/Text2Human
StyleGAN-Human: A Data-Centric Odyssey of Human Generationhttp://arxiv.org/abs/2204.11823
3D-Aware Semantic-Guided Generative Model for Human Synthesishttp://arxiv.org/abs/2112.01422
InsetGAN for Full-Body Image Generationhttp://arxiv.org/abs/2203.07293
Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesishttp://arxiv.org/abs/1909.12224
impersonatorhttps://github.com/svip-lab/impersonator
SMPLpix: Neural Avatars from 3D Human Modelshttp://arxiv.org/abs/2008.06872
smplpixhttps://github.com/sergeyprokudin/smplpix
Neural Articulated Radiance Fieldhttps://arxiv.org/abs/2104.03110v2
Unsupervised Learning of Efficient Geometry-Aware Neural Articulated Representationshttp://arxiv.org/abs/2204.08839
ENARF-GANhttps://github.com/nogu-atsu/ENARF-GAN
Generative Neural Articulated Radiance Fieldshttp://arxiv.org/abs/2206.14314
gnarfhttp://www.computationalimaging.org/publications/gnarf/
AvatarGen: A 3D Generative Model for Animatable Human Avatarshttp://arxiv.org/abs/2208.00561
AvatarGenhttps://github.com/jfzhang95/AvatarGen
EVA3D: Compositional 3D Human Generation from 2D Image Collectionshttp://arxiv.org/abs/2210.04888
https://github.com/PeterouZh/Deep_Generative_Models#body-from-video
SelfRecon: Self Reconstruction Your Digital Avatar from Monocular Videohttp://arxiv.org/abs/2201.12792
https://github.com/PeterouZh/Deep_Generative_Models#3dmm-face
https://github.com/tencent-ailab/hifi3dfacehttps://github.com/tencent-ailab/hifi3dface
https://github.com/ascust/3DMM-Fitting-Pytorchhttps://github.com/ascust/3DMM-Fitting-Pytorch
Neural Head Reenactment with Latent Pose Descriptorshttp://arxiv.org/abs/2004.12000
latent-pose-reenactmenthttps://github.com/shrubb/latent-pose-reenactment
Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometryhttp://arxiv.org/abs/2110.09772
REALY: Rethinking the Evaluation of 3D Face Reconstructionhttp://arxiv.org/abs/2203.09729
REALYhttps://github.com/czh-98/REALY
https://github.com/PeterouZh/Deep_Generative_Models#3d-face-avatars
https://github.com/TimoBolkart/BFM_to_FLAMEhttps://github.com/TimoBolkart/BFM_to_FLAME
https://github.com/HavenFeng/photometric_optimizationhttps://github.com/HavenFeng/photometric_optimization
https://github.com/soubhiksanyal/FLAME_PyTorchhttps://github.com/soubhiksanyal/FLAME_PyTorch
https://github.com/Azmarie/Face-Morphinghttps://github.com/Azmarie/Face-Morphing
A Morphable Model for the Synthesis of 3D Faceshttps://doi.org/10.1145/311535.311556
Learning a Model of Facial Shape and Expression from 4D Scanshttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
FLAME-in-NeRF : Neural Control of Radiance Fields for Free View Face Animationhttp://arxiv.org/abs/2108.04913
Learning a Model of Facial Shape and Expression from 4D Scanshttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
EMOCA: Emotion Driven Monocular Face Capture and Animationhttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
emocahttps://github.com/radekd91/emoca
FaceVerse: A Fine-Grained and Detail-Controllable 3D Face Morphable Model from a Hybrid Datasethttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
I M Avatar: Implicit Morphable Head Avatars from Videoshttp://arxiv.org/abs/2112.07471
IMavatarhttps://github.com/zhengyuf/IMavatar
Neural Head Avatars from Monocular RGB Videoshttp://arxiv.org/abs/2112.01554
neural-head-avatarshttps://github.com/philgras/neural-head-avatars
PVA: Pixel-Aligned Volumetric Avatarshttp://arxiv.org/abs/2101.02697
AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesishttp://arxiv.org/abs/2103.11078
Semantic-Aware Implicit Neural Audio-Driven Video Portrait Generationhttp://arxiv.org/abs/2201.07786
HeadGAN: One-Shot Neural Head Synthesis and Editinghttp://arxiv.org/abs/2012.08261
KeypointNeRF: Generalizing Image-Based Volumetric Avatars Using Relative Spatial Encoding of Keypointshttp://arxiv.org/abs/2205.04992
Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Sethttp://arxiv.org/abs/1903.08527
Deep3DFaceRecon_pytorchhttps://github.com/sicxu/Deep3DFaceRecon_pytorch
https://github.com/PeterouZh/Deep_Generative_Models#stylization
Unified Implicit Neural Stylizationhttp://arxiv.org/abs/2204.01943
ARF: Artistic Radiance Fieldshttp://arxiv.org/abs/2206.06360
ARF-svox2https://github.com/Kai-46/ARF-svox2
UPST-NeRF: Universal Photorealistic Style Transfer of Neural Radiance Fields for 3D Scenehttp://arxiv.org/abs/2208.07059
UPST-NeRFhttps://github.com/semchan/UPST-NeRF
https://github.com/PeterouZh/Deep_Generative_Models#face-style
Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transferhttp://arxiv.org/abs/2203.13248
DualStyleGANhttps://github.com/williamyang1991/DualStyleGAN
Stitch It in Time: GAN-Based Facial Editing of Real Videoshttp://arxiv.org/abs/2201.08361
STIThttps://github.com/rotemtzaban/STIT
Fix the Noise: Disentangling Source Feature for Transfer Learning of StyleGANhttp://arxiv.org/abs/2204.14079
FixNoisehttps://github.com/LeeDongYeun/FixNoise
AnimeCeleb: Large-Scale Animation CelebHeads Dataset for Head Reenactmenthttp://arxiv.org/abs/2111.07640
AnimeCelebhttps://github.com/kangyeolk/AnimeCeleb
DCT-Net: Domain-Calibrated Translation for Portrait Stylizationhttp://arxiv.org/abs/2207.02426
DCT-Nethttps://github.com/menyifang/DCT-Net
VToonify: Controllable High-Resolution Portrait Video Style Transferhttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
VToonifyhttps://github.com/williamyang1991/VToonify
BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generationhttps://arxiv.org/abs/2110.11728v1
BlendGANhttps://github.com/onion-liu/BlendGAN
Unpaired Cartoon Image Synthesis via Gated Cycle Mappinghttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
https://github.com/PeterouZh/Deep_Generative_Models#face-animation
Thin-Plate Spline Motion Model for Image Animationhttp://arxiv.org/abs/2203.14367
Depth-Aware Generative Adversarial Network for Talking Head Video Generationhttp://arxiv.org/abs/2203.06605
DaGANhttps://github.com/harlanhong/CVPR2022-DaGAN
https://github.com/PeterouZh/Deep_Generative_Models#renderer--regularization
https://github.com/ventusff/neureconhttps://github.com/ventusff/neurecon
Implicit Geometric Regularization for Learning Shapeshttp://arxiv.org/abs/2002.10099
Neural 3D Scene Reconstruction with the Manhattan-World Assumptionhttp://arxiv.org/abs/2205.02836
manhattan_sdfhttps://github.com/zju3dv/manhattan_sdf
Differentiable Signed Distance Function Renderinghttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
sdfhttps://github.com/lucidrains/differentiable-SDF-pytorch
NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-View Reconstructionhttp://arxiv.org/abs/2106.10689
NeuShttps://github.com/Totoro97/NeuS
SNeS: Learning Probably Symmetric Neural Surfaces from Incomplete Datahttps://arxiv.org/abs/2206.06340v1
sneshttps://github.com/eldar/snes
Volume Rendering of Neural Implicit Surfaceshttp://arxiv.org/abs/2106.12052
volsdfhttps://github.com/lioryariv/volsdf
Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearancehttps://arxiv.org/abs/2003.09852v3
idrhttps://github.com/lioryariv/idr
Multi-View Mesh Reconstruction With Neural Deferred Shadinghttps://openaccess.thecvf.com/content/CVPR2022/html/Worchel_Multi-View_Mesh_Reconstruction_With_Neural_Deferred_Shading_CVPR_2022_paper.html
neural-deferred-shadinghttps://github.com/fraunhoferhhi/neural-deferred-shading
IRON: Inverse Rendering by Optimizing Neural SDFs and Materials from Photometric Imageshttp://arxiv.org/abs/2204.02232
IRONhttps://github.com/Kai-46/IRON
UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstructionhttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
unisurfhttps://github.com/autonomousvision/unisurf
MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstructionhttps://arxiv.org/abs/2206.00665v1
Direct Voxel Grid Optimization: Super-Fast Convergence for Radiance Fields Reconstructionhttp://arxiv.org/abs/2111.11215
DirectVoxGOhttps://github.com/sunset1995/DirectVoxGO
Improved Direct Voxel Grid Optimization for Radiance Fields Reconstructionhttp://arxiv.org/abs/2206.05085
Improved Surface Reconstruction Using High-Frequency Detailshttp://arxiv.org/abs/2206.07850
InfoNeRF: Ray Entropy Minimization for Few-Shot Neural Volume Renderinghttp://arxiv.org/abs/2112.15399
InfoNeRFhttps://github.com/mjmjeong/InfoNeRF
Improving Neural Implicit Surfaces Geometry with Patch Warpinghttp://arxiv.org/abs/2112.09648
NeuralWarphttps://github.com/fdarmon/NeuralWarp
SparseNeuS: Fast Generalizable Neural Surface Reconstruction from Sparse Viewshttp://arxiv.org/abs/2206.05737
SparseNeuShttps://github.com/xxlong0/SparseNeuS
NeuMeshhttps://github.com/zju3dv/NeuMesh
Neural Density-Distance Fieldshttp://arxiv.org/abs/2207.14455
neddfhttps://github.com/ueda0319/neddf
Neural 3D Reconstruction in the Wildhttp://arxiv.org/abs/2205.12955
NeuralRecon-Whttps://github.com/zju3dv/NeuralRecon-W
KeypointNeRF: Generalizing Image-Based Volumetric Avatars Using Relative Spatial Encoding of Keypointshttp://arxiv.org/abs/2205.04992
KeypointNeRFhttps://github.com/facebookresearch/KeypointNeRF
GO-Surf: Neural Feature Grid Optimization for Fast, High-Fidelity RGB-D Surface Reconstructionhttp://arxiv.org/abs/2206.14735
go-surfhttps://github.com/JingwenWang95/go-surf
https://github.com/PeterouZh/Deep_Generative_Models#material-and-lighting
NeILF: Neural Incident Light Field for Physically-Based Material Estimationhttp://arxiv.org/abs/2203.07182
neilfhttps://github.com/apple/ml-neilf
NeRF-OSRhttps://github.com/r00tman/NeRF-OSR
https://github.com/PeterouZh/Deep_Generative_Models#motion
https://github.com/xianfei/SysMocaphttps://github.com/xianfei/SysMocap
https://github.com/zju3dv/EasyMocaphttps://github.com/zju3dv/EasyMocap
https://github.com/EricGuo5513/HumanML3Dhttps://github.com/EricGuo5513/HumanML3D
GANimator: Neural Motion Synthesis from a Single Sequencehttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
ganimatorhttps://github.com/PeizhuoLi/ganimator
watch-it-movehttps://github.com/NVlabs/watch-it-move
Learn to Dance with AIST++: Music Conditioned 3D Dance Generationhttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
Talking Head(?) Anime from a Single Image 3: Now the Body Toohttp://pkhungurn.github.io/talking-head-anime-3/
talking-head-animehttps://github.com/pkhungurn/talking-head-anime-3-demo
PhysCap: Physically Plausible Monocular 3D Motion Capture in Real Timehttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
The Wanderings of Odysseus in 3D Sceneshttp://arxiv.org/abs/2112.09251
GAMMAhttps://github.com/yz-cnsdqz/GAMMA-release
Adversarial Parametric Pose Priorhttp://arxiv.org/abs/2112.04203
adv_param_pose_priorhttps://github.com/cvlab-epfl/adv_param_pose_prior
AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatarshttp://arxiv.org/abs/2205.08535
AvatarCLIPhttps://github.com/hongfz16/AvatarCLIP
somahttps://github.com/nghorbani/soma
MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Modelhttp://arxiv.org/abs/2208.15001
MotionDiffusehttps://github.com/mingyuan-zhang/MotionDiffuse
TEACH: Temporal Action Composition for 3D Humanshttp://arxiv.org/abs/2209.04066
teachhttps://github.com/athn-nik/teach
TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Textshttp://arxiv.org/abs/2207.01696
TM2Thttps://github.com/EricGuo5513/TM2T
https://github.com/PeterouZh/Deep_Generative_Models#shape-generation
Learning Implicit Fields for Generative Shape Modelinghttp://arxiv.org/abs/1812.02822
https://github.com/PeterouZh/Deep_Generative_Models#smpl-estimation
https://github.com/open-mmlab/mmhuman3dhttps://github.com/open-mmlab/mmhuman3d
End-to-End Recovery of Human Shape and Posehttp://arxiv.org/abs/1712.06584
VIBE: Video Inference for Human Body Pose and Shape Estimationhttp://arxiv.org/abs/1912.05656
VIBEhttps://github.com/mkocabas/VIBE
TransPose: Real-Time 3D Human Translation and Pose Estimation with Six Inertial Sensorshttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
TransPosehttps://github.com/Xinyu-Yi/TransPose
Monocular Expressive Body Regression through Body-Driven Attentionhttps://expose.is.tue.mpg.de
exposehttps://github.com/vchoutas/expose
Human Mesh Recovery from Multiple Shotshttp://arxiv.org/abs/2012.09843
multishothttps://github.com/geopavlakos/multishot
Learned Vertex Descent: A New Direction for 3D Human Model Fittinghttp://arxiv.org/abs/2205.06254
LVDhttps://github.com/enriccorona/LVD
DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimationhttp://arxiv.org/abs/2203.08713
DeciWatchhttps://github.com/cure-lab/DeciWatch
PARE: Part Attention Regressor for 3D Human Body Estimationhttp://arxiv.org/abs/2104.08527
PAREhttps://github.com/mkocabas/PARE
Cross-Attention of Disentangled Modalities for 3D Human Mesh Recovery with Transformershttp://arxiv.org/abs/2207.13820
FastMETROhttps://github.com/postech-ami/FastMETRO
https://github.com/PeterouZh/Deep_Generative_Models#segmentation-1
https://github.com/facebookresearch/MaskFormerhttps://github.com/facebookresearch/MaskFormer
Real-Time High-Resolution Background Mattinghttp://arxiv.org/abs/2012.07810
BackgroundMattingV2https://github.com/PeterL1n/BackgroundMattingV2
Robust High-Resolution Video Matting with Temporal Guidancehttp://arxiv.org/abs/2108.11515
RobustVideoMattinghttps://github.com/PeterL1n/RobustVideoMatting
https://github.com/PeterouZh/Deep_Generative_Models#datasets-1
https://github.com/karfly/human36m-camera-parametershttps://github.com/karfly/human36m-camera-parameters
https://github.com/deepimagination/TalkingHead-1KHhttps://github.com/deepimagination/TalkingHead-1KH
Structured Local Radiance Fields for Human Avatar Modelinghttp://arxiv.org/abs/2203.14478
THUman4.0-Datasethttps://github.com/ZhengZerong/THUman4.0-Dataset
Multiface: A Dataset for Neural Face Renderinghttps://arxiv.org/abs/2207.11243v1
multifacehttps://github.com/facebookresearch/multiface
ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representationshttp://arxiv.org/abs/2203.14510
ImFacehttps://github.com/MingwuZheng/ImFace
https://github.com/PeterouZh/Deep_Generative_Models#flame-estimation
Towards Metrical Reconstruction of Human Faceshttp://arxiv.org/abs/2204.06607
MICAhttps://github.com/Zielon/MICA
https://github.com/PeterouZh/Deep_Generative_Models#dog-estimation
barc_releasehttps://github.com/runa91/barc_release
https://github.com/PeterouZh/Deep_Generative_Models#panoptic
Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene Segmentationhttp://arxiv.org/abs/2203.15224
PanopticNeRFhttps://github.com/fuxiao0719/PanopticNeRF
https://github.com/PeterouZh/Deep_Generative_Models#sdf
https://github.com/facebookresearch/pifuhdhttps://github.com/facebookresearch/pifuhd
https://github.com/pmneila/PyMCubeshttps://github.com/pmneila/PyMCubes
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationhttp://arxiv.org/abs/1901.05103
DeepSDFhttps://github.com/facebookresearch/DeepSDF
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modelinghttp://arxiv.org/abs/1610.07584
Occupancy Networks: Learning 3D Reconstruction in Function Spacehttp://arxiv.org/abs/1812.03828
PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitizationhttp://arxiv.org/abs/1905.05172
Deep Meta Functionals for Shape Representationhttp://arxiv.org/abs/1908.06277
https://github.com/PeterouZh/Deep_Generative_Models#3d-2
Escaping Plato’s Cave: 3D Shape From Adversarial Renderinghttp://arxiv.org/abs/1811.11606
StyleRig: Rigging StyleGAN for 3D Control over Portrait Imageshttp://arxiv.org/abs/2004.00121
Exemplar-Based 3D Portrait Stylizationhttp://arxiv.org/abs/2104.14559
githubhttps://github.com/halfjoe/3D-Portrait-Stylization
Landmark Detection and 3D Face Reconstruction for Caricature Using a Nonlinear Parametric Modelhttp://arxiv.org/abs/2004.09190
CaricatureFacehttps://github.com/Juyong/CaricatureFace
SofGAN: A Portrait Image Generator with Dynamic Stylinghttp://arxiv.org/abs/2007.03780
sofganhttps://github.com/apchenstu/sofgan
FreeStyleGAN: Free-View Editable Portrait Rendering with the Camera Manifoldhttp://arxiv.org/abs/2109.09378
PIRenderer: Controllable Portrait Image Generation via Semantic Neural Renderinghttp://arxiv.org/abs/2109.08379
PIRenderhttps://github.com/RenYurui/PIRender
https://github.com/PeterouZh/Deep_Generative_Models#point-cloud
Point-Based Modeling of Human Clothinghttps://openaccess.thecvf.com/content/ICCV2021/html/Zakharkin_Point-Based_Modeling_of_Human_Clothing_ICCV_2021_paper.html
ADOP: Approximate Differentiable One-Pixel Point Renderinghttp://arxiv.org/abs/2110.06635
https://github.com/PeterouZh/Deep_Generative_Models#stylization-1
Learning to Stylize Novel Viewshttp://arxiv.org/abs/2105.13509
stylescenehttps://github.com/hhsinping/stylescene
https://github.com/PeterouZh/Deep_Generative_Models#datasets-2
https://github.com/ofirkris/Faces-datasetshttps://github.com/ofirkris/Faces-datasets
Common Objects in 3D: Large-Scale Learning and Evaluation of Real-Life 3D Category Reconstructionhttp://arxiv.org/abs/2109.00512
A 3D Face Model for Pose and Illumination Invariant Face Recognitionhttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
BFMhttps://faces.dmi.unibas.ch/bfm/main.php?nav=1-2&id=downloads
SfSNet: Learning Shape, Reflectance and Illuminance of Faces in the Wildhttp://arxiv.org/abs/1712.01261
https://github.com/PeterouZh/Deep_Generative_Models#3d-aware-image-synthesis-ref
Visual Object Networks: Image Generation with Disentangled 3D Representationhttp://arxiv.org/abs/1812.02725
Escaping Plato’s Cave: 3D Shape From Adversarial Renderinghttp://arxiv.org/abs/1811.11606
HoloGAN: Unsupervised Learning of 3D Representations from Natural Imageshttp://arxiv.org/abs/1904.01326
https://github.com/PeterouZh/Deep_Generative_Models#face-1
https://github.com/PeterouZh/Deep_Generative_Models#tools-1
https://github.com/wuhuikai/FaceSwaphttps://github.com/wuhuikai/FaceSwap
https://github.com/hysts/anime-face-detectorhttps://github.com/hysts/anime-face-detector
https://github.com/qq775193759/3D-CariGANhttps://github.com/qq775193759/3D-CariGAN
https://github.com/yeemachine/kalidokithttps://github.com/yeemachine/kalidokit
https://github.com/sicxu/Deep3DFaceRecon_pytorchhttps://github.com/sicxu/Deep3DFaceRecon_pytorch
https://github.com/happy-jihye/face-vid2vid-demohttps://github.com/happy-jihye/face-vid2vid-demo
https://github.com/PeterouZh/Deep_Generative_Models#edit
FaceEraser: Removing Facial Parts for Augmented Realityhttp://arxiv.org/abs/2109.10760
DyStyle: Dynamic Neural Network for Multi-Attribute-Conditioned Style Editinghttp://arxiv.org/abs/2109.10737
StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generatorshttp://arxiv.org/abs/2108.00946
Beholder-GAN: Generation and Beautification of Facial Images with Conditioning on Their Beauty Levelhttp://arxiv.org/abs/1902.02593
Mind the Gap: Domain Gap Control for Single Shot Domain Adaptation for Generative Adversarial Networkshttp://arxiv.org/abs/2110.08398
Fine-Grained Control of Artistic Styles in Image Generationhttp://arxiv.org/abs/2110.10278
https://github.com/PeterouZh/Deep_Generative_Models#anime-face
https://github.com/Sxela/ArcaneGANhttps://github.com/Sxela/ArcaneGAN
https://github.com/mchong6/GANsNRoseshttps://github.com/mchong6/GANsNRoses
https://github.com/FilipAndersson245/cartoon-ganhttps://github.com/FilipAndersson245/cartoon-gan
https://github.com/venture-anime/cartoongan-pytorchhttps://github.com/venture-anime/cartoongan-pytorch
AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generationhttp://arxiv.org/abs/2102.12593
AnimeGANv2https://github.com/TachibanaYoshino/AnimeGANv2
Learning to Cartoonize Using White-Box Cartoon Representationshttps://ieeexplore.ieee.org/document/9157493/
White-box-Cartoonizationhttps://github.com/SystemErrorWang/White-box-Cartoonization
Generative Adversarial Networks for Photo to Hayao Miyazaki Style Cartoonshttp://arxiv.org/abs/2005.07702
https://github.com/PeterouZh/Deep_Generative_Models#3dmm
https://github.com/lattas/AvatarMehttps://github.com/lattas/AvatarMe
A Morphable Model for the Synthesis of 3D Faceshttps://doi.org/10.1145/311535.311556
https://github.com/PeterouZh/Deep_Generative_Models#face-2
SketchHairSalon: Deep Sketch-Based Hair Image Synthesishttp://arxiv.org/abs/2109.07874
https://github.com/PeterouZh/Deep_Generative_Models#face-alignment
Face Alignment Across Large Poses: A 3D Solutionhttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
https://github.com/PeterouZh/Deep_Generative_Models#face-recognition
High-Fidelity Pose and Expression Normalization for Face Recognition in the Wildhttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
https://github.com/PeterouZh/Deep_Generative_Models#face-swapping
https://github.com/mindslab-ai/hififacehttps://github.com/mindslab-ai/hififace
https://github.com/PeterouZh/Deep_Generative_Models#3d-3
Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wildhttp://arxiv.org/abs/1911.11130
unsup3dhttps://github.com/elliottwu/unsup3d
Do 2D GANs Know 3D Shape? Unsupervised 3D Shape Reconstruction from 2D Image GANshttp://arxiv.org/abs/2011.00844
GAN2Shapehttps://github.com/XingangPan/GAN2Shape
A Geometric Analysis of Deep Generative Image Models and Its Applicationshttps://openreview.net/forum?id=GH7QRzUDdXG
Lifting 2D StyleGAN for 3D-Aware Face Generationhttp://arxiv.org/abs/2011.13126
LiftedGANhttps://github.com/seasonSH/LiftedGAN
Image GANs Meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Renderinghttp://arxiv.org/abs/2010.09125
Neural 3D Mesh Rendererhttp://arxiv.org/abs/1711.07566
Fast-GANFIT: Generative Adversarial Network for High Fidelity 3D Face Reconstructionhttp://arxiv.org/abs/2105.07474
Inverting Generative Adversarial Renderer for Face Reconstructionhttp://arxiv.org/abs/2105.02431
StyleRendererhttps://github.com/WestlyPark/StyleRenderer
Learning to Aggregate and Personalize 3D Face from In-the-Wild Photo Collectionhttp://arxiv.org/abs/2106.07852
Subdivision-Based Mesh Convolution Networkshttp://arxiv.org/abs/2106.02285
Learning to Aggregate and Personalize 3D Face from In-the-Wild Photo Collectionhttp://arxiv.org/abs/2106.07852
To Fit or Not to Fit: Model-Based Face Reconstruction and Occlusion Segmentation from Weak Supervisionhttp://arxiv.org/abs/2106.09614
Unsupervised Learning of Depth and Depth-of-Field Effect from Natural Images with Aperture Rendering Generative Adversarial Networkshttp://arxiv.org/abs/2106.13041
DOVE: Learning Deformable 3D Objects by Watching Videoshttp://arxiv.org/abs/2107.10844
De-Rendering the World’s Revolutionary Artefactshttp://arxiv.org/abs/2104.03954
Learning Generative Models of Textured 3D Meshes from Real-World Imageshttp://arxiv.org/abs/2103.15627
Toward Realistic Single-View 3D Object Reconstruction with Unsupervised Learning from Multiple Imageshttp://arxiv.org/abs/2109.02288
https://github.com/PeterouZh/Deep_Generative_Models#da
Semi-Supervised Domain Adaptation via Adaptive and Progressive Feature Alignmenthttp://arxiv.org/abs/2106.02845
Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentationhttp://arxiv.org/abs/2101.10979
https://github.com/PeterouZh/Deep_Generative_Models#data
https://github.com/koaning/doubtlabhttps://github.com/koaning/doubtlab
Semi-Supervised Active Learning with Temporal Output Discrepancyhttp://arxiv.org/abs/2107.14153
Mean Teachers Are Better Role Models: Weight-Averaged Consistency Targets Improve Semi-Supervised Deep Learning Resultshttp://arxiv.org/abs/1703.01780
When Deep Learners Change Their Mind: Learning Dynamics for Active Learninghttp://arxiv.org/abs/2107.14707
On The State of Data In Computer Vision: Human Annotations Remain Indispensable for Developing Deep Learning Modelshttp://arxiv.org/abs/2108.00114
StyleAugment: Learning Texture De-Biased Representations by Style Augmentation without Pre-Defined Textureshttp://arxiv.org/abs/2108.10549
Multi-Task Self-Training for Learning General Representationshttp://arxiv.org/abs/2108.11353
OOWL500: Overcoming Dataset Collection Bias in the Wildhttp://arxiv.org/abs/2108.10992
Ghost Loss to Question the Reliability of Training Datahttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
Revisiting 3D ResNets for Video Recognitionhttp://arxiv.org/abs/2109.01696
Revisiting ResNets: Improved Training and Scaling Strategieshttp://arxiv.org/abs/2103.07579
Learning Fast Sample Re-Weighting Without Reward Datahttp://arxiv.org/abs/2109.03216
How Important Is Importance Sampling for Deep Budgeted Training?http://arxiv.org/abs/2110.14283
https://github.com/PeterouZh/Deep_Generative_Models#cnn--bn
https://github.com/PeterouZh/Deep_Generative_Models#light-architecture
https://github.com/yoshitomo-matsubara/torchdistillhttps://github.com/yoshitomo-matsubara/torchdistill
https://github.com/milesial/Pytorch-UNethttps://github.com/milesial/Pytorch-UNet
Network Augmentation for Tiny Deep Learninghttp://arxiv.org/abs/2110.08890
Non-Deep Networkshttp://arxiv.org/abs/2110.07641
When to Prune? A Policy towards Early Structural Pruninghttp://arxiv.org/abs/2110.12007
ConformalLayers: A Non-Linear Sequential Neural Network with Associative Layershttp://arxiv.org/abs/2110.12108
CHIP: CHannel Independence-Based Pruning for Compact Neural Networkshttp://arxiv.org/abs/2110.13981
Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Traininghttp://arxiv.org/abs/2102.02887
https://github.com/PeterouZh/Deep_Generative_Models#antialiased-cnns
Making Convolutional Networks Shift-Invariant Againhttp://arxiv.org/abs/1904.11486
Group Equivariant Convolutional Networkshttp://arxiv.org/abs/1602.07576
Harmonic Networks: Deep Translation and Rotation Equivariancehttp://arxiv.org/abs/1612.04642
Learning Steerable Filters for Rotation Equivariant CNNshttp://arxiv.org/abs/1711.07289
https://github.com/PeterouZh/Deep_Generative_Models#architecture
Beyond BatchNorm: Towards a General Understanding of Normalization in Deep Learninghttp://arxiv.org/abs/2106.05956
R-Drop: Regularized Dropout for Neural Networkshttp://arxiv.org/abs/2106.14448
Switchable Whitening for Deep Representation Learninghttp://arxiv.org/abs/1904.09739
Positional Normalizationhttp://arxiv.org/abs/1907.04312
On Feature Normalization and Data Augmentationhttp://arxiv.org/abs/2002.11102
Channel Equilibrium Networks for Learning Deep Representationhttp://arxiv.org/abs/2003.00214
Representative Batch Normalization with Feature Calibrationhttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
EPSANet: An Efficient Pyramid Squeeze Attention Block on Convolutional Neural Networkhttp://arxiv.org/abs/2105.14447
Bias Loss for Mobile Neural Networkshttp://arxiv.org/abs/2107.11170
Compositional Models: Multi-Task Learning and Knowledge Transfer with Modular Networkshttp://arxiv.org/abs/2107.10963
Log-Polar Space Convolution for Convolutional Neural Networkshttp://arxiv.org/abs/2107.11943
Decoupled Dynamic Filter Networkshttp://arxiv.org/abs/2104.14107
Spectral Leakage and Rethinking the Kernel Size in CNNshttp://arxiv.org/abs/2101.10143
Learning with Noisy Labels via Sparse Regularizationhttp://arxiv.org/abs/2108.00192
Impact of Aliasing on Generalization in Deep Convolutional Networkshttp://arxiv.org/abs/2108.03489
Orthogonal Over-Parameterized Traininghttp://arxiv.org/abs/2004.04690
Multiplying Matrices Without Multiplyinghttp://arxiv.org/abs/2106.10860
AASeg: Attention Aware Network for Real Time Semantic Segmentationhttp://arxiv.org/abs/2108.04349
MicroNet: Improving Image Recognition with Extremely Low FLOPshttp://arxiv.org/abs/2108.05894
Contextual Convolutional Neural Networkshttp://arxiv.org/abs/2108.07387
Torch.Manual_seed(3407) Is All You Need: On the Influence of Random Seeds in Deep Learning Architectures for Computer Visionhttp://arxiv.org/abs/2109.08203
KATANA: Simple Post-Training Robustness Using Test Time Augmentationshttp://arxiv.org/abs/2109.08191
Global Pooling, More than Meets the Eye: Position Information Is Encoded Channel-Wise in CNNshttp://arxiv.org/abs/2108.07884
A ConvNet for the 2020shttp://arxiv.org/abs/2201.03545
ConvNeXthttps://github.com/facebookresearch/ConvNeXt
https://github.com/PeterouZh/Deep_Generative_Models#compression-1
AdaPruner: Adaptive Channel Pruning and Effective Weights Inheritancehttp://arxiv.org/abs/2109.06397
https://github.com/PeterouZh/Deep_Generative_Models#detection-2
Anchor DETR: Query Design for Transformer-Based Detectorhttp://arxiv.org/abs/2109.07107
Detecting Twenty-Thousand Classes Using Image-Level Supervisionhttp://arxiv.org/abs/2201.02605
https://github.com/PeterouZh/Deep_Generative_Models#segmentation-2
https://github.com/xuebinqin/U-2-Net#usage-for-portrait-generationhttps://github.com/xuebinqin/U-2-Net#usage-for-portrait-generation
Robust High-Resolution Video Matting with Temporal Guidancehttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
https://github.com/PeterouZh/Deep_Generative_Models#mlp
ResMLP: Feedforward Networks for Image Classification with Data-Efficient Traininghttp://arxiv.org/abs/2105.03404
ConvMLP: Hierarchical Convolutional MLPs for Visionhttp://arxiv.org/abs/2109.04454
A Battle of Network Structures: An Empirical Study of CNN, Transformer, and MLPhttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
Sparse-MLP: A Fully-MLP Architecture with Conditional Computationhttp://arxiv.org/abs/2109.02008
MLP-Mixer: An All-MLP Architecture for Visionhttps://arxiv.org/abs/2105.01601v1
CycleMLP: A MLP-like Architecture for Dense Predictionhttp://arxiv.org/abs/2107.10224
https://github.com/PeterouZh/Deep_Generative_Models#transformer-1
https://github.com/xxxnell/how-do-vits-workhttps://github.com/xxxnell/how-do-vits-work
https://github.com/hamidkazemi22/vit-visualizationhttps://github.com/hamidkazemi22/vit-visualization
Training Data-Efficient Image Transformers & Distillation through Attentionhttp://arxiv.org/abs/2012.12877
deithttps://github.com/facebookresearch/deit
Intriguing Properties of Vision Transformershttp://arxiv.org/abs/2105.10497
CogView: Mastering Text-to-Image Generation via Transformershttp://arxiv.org/abs/2105.13290
An Image Is Worth 16x16 Words: Transformers for Image Recognition at Scalehttp://arxiv.org/abs/2010.11929
Scaling Vision Transformershttp://arxiv.org/abs/2106.04560
IA-RED$^2$: Interpretability-Aware Redundancy Reduction for Vision Transformershttp://arxiv.org/abs/2106.12620
Rethinking and Improving Relative Position Encoding for Vision Transformerhttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
Go Wider Instead of Deeperhttp://arxiv.org/abs/2107.11817
A Unified Efficient Pyramid Transformer for Semantic Segmentationhttp://arxiv.org/abs/2107.14209
Conditional DETR for Fast Training Convergencehttp://arxiv.org/abs/2108.06152
Sketch Your Own GANhttp://arxiv.org/abs/2108.02774
CrossFormer: A Versatile Vision Transformer Based on Cross-Scale Attentionhttp://arxiv.org/abs/2108.00154
Uformer: A General U-Shaped Transformer for Image Restorationhttp://arxiv.org/abs/2106.03106
ConvNets vs. Transformers: Whose Visual Representations Are More Transferable?http://arxiv.org/abs/2108.05305
Mobile-Former: Bridging MobileNet and Transformerhttp://arxiv.org/abs/2108.05895
SOTR: Segmenting Objects with Transformershttp://arxiv.org/abs/2108.06747
Video Transformer Networkhttp://arxiv.org/abs/2102.00719
Do Vision Transformers See Like Convolutional Neural Networks?http://arxiv.org/abs/2108.08810
UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-Wise Perspective with Transformerhttp://arxiv.org/abs/2109.04335
$\infty$-Former: Infinite Memory Transformerhttp://arxiv.org/abs/2109.00301
PnP-DETR: Towards Efficient Visual Analysis with Transformershttp://arxiv.org/abs/2109.07036
MobileViT: Light-Weight, General-Purpose, and Mobile-Friendly Vision Transformerhttp://arxiv.org/abs/2110.02178
MetaFormer Is Actually What You Need for Visionhttp://arxiv.org/abs/2111.11418
Restormer: Efficient Transformer for High-Resolution Image Restorationhttp://arxiv.org/abs/2111.09881
Restormerhttps://github.com/swz30/Restormer
An Empirical Study of Training Self-Supervised Vision Transformershttp://arxiv.org/abs/2104.02057
When Vision Transformers Outperform ResNets without Pre-Training or Strong Data Augmentationshttps://arxiv.org/abs/2106.01548v2
Visual Attention Networkhttp://arxiv.org/abs/2202.09741
https://github.com/PeterouZh/Deep_Generative_Models#ssl
https://github.com/ucasligang/awesome-MIMhttps://github.com/ucasligang/awesome-MIM
Emerging Properties in Self-Supervised Vision Transformershttp://arxiv.org/abs/2104.14294
dinohttps://github.com/facebookresearch/dino
What Is Considered Complete for Visual Recognition?http://arxiv.org/abs/2105.13978
On the Efficacy of Small Self-Supervised Contrastive Models without Distillation Signalshttp://arxiv.org/abs/2107.14762
Improving Contrastive Learning by Visualizing Feature Transformationhttp://arxiv.org/abs/2108.02982
Scale Efficiently: Insights from Pre-Training and Fine-Tuning Transformershttp://arxiv.org/abs/2109.10686
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labelinghttp://arxiv.org/abs/2110.08263
BEiT: BERT Pre-Training of Image Transformershttp://arxiv.org/abs/2106.08254
Parametric Contrastive Learninghttp://arxiv.org/abs/2107.12028
ImageNet-21K Pretraining for the Masseshttp://arxiv.org/abs/2104.10972
ImageNet21Khttps://github.com/Alibaba-MIIL/ImageNet21K
ML-Decoder: Scalable and Versatile Classification Headhttp://arxiv.org/abs/2111.12933
ML_Decoderhttps://github.com/Alibaba-MIIL/ML_Decoder
Asymmetric Loss For Multi-Label Classificationhttp://arxiv.org/abs/2009.14119
ASLhttps://github.com/Alibaba-MIIL/ASL
Grounded Language-Image Pre-Traininghttp://arxiv.org/abs/2112.03857
https://github.com/PeterouZh/Deep_Generative_Models#finetune-1
How Transferable Are Features in Deep Neural Networks?http://arxiv.org/abs/1411.1792
https://github.com/PeterouZh/Deep_Generative_Models#positional-encoding
Positional Encoding as Spatial Inductive Bias in GANshttp://arxiv.org/abs/2012.05217
Mind the Pad -- CNNs Can Develop Blind Spotshttp://arxiv.org/abs/2010.02178
How Much Position Information Do Convolutional Neural Networks Encode?http://arxiv.org/abs/2001.08248
On Translation Invariance in CNNs: Convolutional Layers Can Exploit Absolute Spatial Locationhttp://arxiv.org/abs/2003.07064
Rethinking and Improving Relative Position Encoding for Vision Transformerhttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
A Structured Dictionary Perspective on Implicit Neural Representationshttp://arxiv.org/abs/2112.01917
https://github.com/PeterouZh/Deep_Generative_Models#nas
https://github.com/PeterouZh/Deep_Generative_Models#nas-cls
Neural Architecture Search with Reinforcement Learninghttps://arxiv.org/abs/1611.01578v2
Learning Transferable Architectures for Scalable Image Recognitionhttp://arxiv.org/abs/1707.07012
Progressive Neural Architecture Searchhttp://arxiv.org/abs/1712.00559
Efficient Neural Architecture Search via Parameter Sharinghttp://arxiv.org/abs/1802.03268
MnasNet: Platform-Aware Neural Architecture Search for Mobilehttp://arxiv.org/abs/1807.11626
DARTS: Differentiable Architecture Searchhttp://arxiv.org/abs/1806.09055
https://github.com/PeterouZh/Deep_Generative_Models#nas-gan
AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networkshttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
GAN Compression: Efficient Architectures for Interactive Conditional GANshttp://openaccess.thecvf.com/content_CVPR_2020/html/Li_GAN_Compression_Efficient_Architectures_for_Interactive_Conditional_GANs_CVPR_2020_paper.html
Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Searchhttp://arxiv.org/abs/2007.09180
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networkshttp://arxiv.org/abs/2006.08198
A Multi-Objective Architecture Search for Generative Adversarial Networkshttps://doi.org/10.1145/3377929.3390004
AutoGAN: Neural Architecture Search for Generative Adversarial Networkshttp://arxiv.org/abs/1908.03835
https://github.com/PeterouZh/Deep_Generative_Models#low-level
https://github.com/PeterouZh/Deep_Generative_Models#super-resolution
https://github.com/nihui/realsr-ncnn-vulkanhttps://github.com/nihui/realsr-ncnn-vulkan
https://github.com/PeterouZh/Deep_Generative_Models#frame-interpolation
FILM: Frame Interpolation for Large Motionhttp://arxiv.org/abs/2202.04901
https://github.com/PeterouZh/Deep_Generative_Models#denoising
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filteringhttps://github.com/PeterouZh/Deep_Generative_Models/blob/main
Towards Flexible Blind JPEG Artifacts Removalhttp://arxiv.org/abs/2109.14573
FBCNNhttps://github.com/jiaxi-jiang/FBCNN
https://github.com/PeterouZh/Deep_Generative_Models#scholar
https://github.com/tangjiapenghttps://github.com/tangjiapeng
Fisher Yuhttps://www.yf.io/
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