{"id":13411669,"url":"https://github.com/lzhbrian/image-to-image-papers","last_synced_at":"2026-02-06T00:01:36.271Z","repository":{"id":48116077,"uuid":"147687846","full_name":"lzhbrian/image-to-image-papers","owner":"lzhbrian","description":"🦓\u003c-\u003e🦒 🌃\u003c-\u003e🌆 A collection of image to image papers with code (constantly 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Code and Slides","Other Useful Resources"],"sub_categories":["Uncategorized","GANs, augmentation, etc.","Something helpful from other areas","Forecast"],"readme":"# Image-to-Image papers\n\nA collection of image-to-image papers.\n\nPapers are ordered in arXiv first version submitting time (if applicable).\n\nFeel free to send a PR or issue.\n\n\n\n__TOC__\n\n- [Supervised](#supervised)\n- [Unsupervised](#unsupervised)\n - [Unsupervised - General](#unsupervised---general)\n - [Unsupervised - Attention/Instance guided](#unsupervised---attentioninstance-guided)\n - [Unsupervised - Many-to-many (Attributes)](#unsupervised---many-to-many-attributes)\n - [Unsupervised - Disentangled (and/or Exemplar guided)](#unsupervised---disentangled-andor-exemplar-guided)\n- [To be classified](#to-be-classified)\n\n\n\n\n## Supervised\n\n| Note | Model | Paper | Conference | paper link | code link |\n| --------------- | ----------------------------------------- | ------------------------------------------------------------ | ---------- | ---------------------------------------------- | ------------------------------------------------------------ |\n| | pix2pix | Image-to-Image Translation with Conditional Adversarial Networks | CVPR 2017 | [1611.07004](https://arxiv.org/abs/1611.07004) | [junyanz/pytorch-CycleGAN-and-pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) |\n| texture guided | TextureGAN | TextureGAN: Controlling Deep Image Synthesis with Texture Patches | CVPR 2018 | [1706.02823](https://arxiv.org/abs/1706.02823) | [janesjanes/Pytorch-TextureGAN](https://github.com/janesjanes/Pytorch-TextureGAN) |\n| | Contextual GAN | Image Generation from Sketch Constraint Using Contextual GAN | ECCV 2018 | [1711.08972](https://arxiv.org/abs/1711.08972) | |\n| | pix2pix-HD | High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs | CVPR 2018 | [1711.11585](https://arxiv.org/abs/1711.11585) | [NVIDIA/pix2pixHD](https://github.com/NVIDIA/pix2pixHD) |\n| one-to-many | BicycleGAN | Toward Multimodal Image-to-Image Translation | NIPS 2017 | [1711.11586](https://arxiv.org/abs/1711.11586) | [junyanz/BicycleGAN](https://github.com/junyanz/BicycleGAN) |\n| keypoint guided | G2-GAN | Geometry Guided Adversarial Facial Expression Synthesis | MM 2018 | [1712.03474](https://arxiv.org/abs/1712.03474) | |\n| | contour2im | Smart, Sparse Contours to Represent and Edit Images | CVPR 2018 | [1712.08232](https://arxiv.org/abs/1712.08232) | [website](https://contour2im.github.io/) |\n| disentangle | Cross-domain disentanglement networks | Image-to-image translation for cross-domain disentanglement | NIPS 2018 | [1805.09730](https://arxiv.org/abs/1805.09730) | |\n| gesture | GestureGAN | GestureGAN for Hand Gesture-to-Gesture Translation in the Wild | MM 2018 | [1808.04859](https://arxiv.org/abs/1808.04859) | [Ha0Tang/GestureGAN](https://github.com/Ha0Tang/GestureGAN) |\n| video | vid2vid | Video-to-Video Synthesis | NIPS 2018 | [1808.06601](https://arxiv.org/abs/1808.06601) | [NVIDIA/vid2vid](https://github.com/NVIDIA/vid2vid) |\n| video | pix2pix-HD + Temporal Smoothing + faceGAN | Everybody Dance Now | ECCVW 2018 | [1808.07371](https://arxiv.org/abs/1808.07371) | [website](https://carolineec.github.io/everybody_dance_now/) |\n| (un)supervised | MSGAN | Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis | CVPR 2019 | [1903.05628](https://arxiv.org/abs/1903.05628) | [HelenMao/MSGAN](https://github.com/HelenMao/MSGAN) |\n| semantic to image | SPADE | Semantic Image Synthesis with Spatially-Adaptive Normalization | CVPR 2019 | [1903.07291](https://arxiv.org/abs/1903.07291) | [NVlabs/SPADE](https://github.com/NVlabs/SPADE) |\n| Edge and color domain to reconstrcut image | PI-REC | PI-REC: Progressive Image Reconstruction Network With Edge and Color Domain | | [1903.10146](https://arxiv.org/abs/1903.10146) | [youyuge34/PI-REC](https://github.com/youyuge34/PI-REC) |\n| Cross-view image translation | SelectionGAN | Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation | CVPR 2019 | [1904.06807](https://arxiv.org/abs/1904.06807) | [Ha0Tang/SelectionGAN](https://github.com/Ha0Tang/SelectionGAN) |\n| keypoint guided | C2-GAN | Cycle In Cycle Generative Adversarial Networks for Keypoint-Guided Image Generation | MM 2019 | [1908.00999](https://arxiv.org/abs/1908.00999) | |\n| Few shot, video | Few-shot vid2vid | Few-shot Video-to-Video Synthesis | NeurIPS 2019 | [1910.12713](https://arxiv.org/abs/1910.12713) | [NVlabs/few-shot-vid2vid](https://github.com/NVlabs/few-shot-vid2vid) |\n\n\n\n## Unsupervised\n\n### Unsupervised - General\n\n| Note | Model | Paper | Conference | paper link | code link |\n| ----------------------------------- | ------------ | ------------------------------------------------------------ | ---------- | ------------------------------------------------------------ | ------------------------------------------------------------ |\n| | DTN | Unsupervised Cross-Domain Image Generation | ICLR 2017 | [1611.02200](https://arxiv.org/abs/1611.02200) | [yunjey/domain-transfer-network (unofficial)](https://github.com/yunjey/domain-transfer-network) |\n| | UNIT | Unsupervised image-to-image translation networks | NIPS 2017 | [1703.00848](https://arxiv.org/abs/1703.00848) | [mingyuliutw/UNIT](https://github.com/mingyuliutw/UNIT) |\n| | DiscoGAN | Learning to Discover Cross-Domain Relations with Generative Adversarial Networks | ICML 2017 | [1703.05192](https://arxiv.org/abs/1703.05192) | [SKTBrain/DiscoGAN](https://github.com/SKTBrain/DiscoGAN) |\n| | CycleGAN | Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks | ICCV 2017 | [1703.10593](https://arxiv.org/abs/1703.10593) | [junyanz/pytorch-CycleGAN-and-pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) |\n| | DualGAN | DualGAN: Unsupervised Dual Learning for Image-to-Image Translation | ICCV 2017 | [1704.02510](https://arxiv.org/abs/1704.02510) | [duxingren14/DualGAN](https://github.com/duxingren14/DualGAN) |\n| | DistanceGAN | One-Sided Unsupervised Domain Mapping | NIPS 2017 | [1706.00826](https://arxiv.org/abs/1706.00826) | [sagiebenaim/DistanceGAN](https://github.com/sagiebenaim/DistanceGAN) |\n| | SisGAN | SisGAN: Semantic Image Synthesis via Adversarial Learning | ICCV 2017 | [1707.06873](https://arxiv.org/abs/1707.06873) | [woozzu/dong_iccv_2017](https://github.com/woozzu/dong_iccv_2017) |\n| semi supervised | Triangle GAN | Triangle Generative Adversarial Networks | NIPS 2017 | [1709.06548](https://arxiv.org/abs/1709.06548) | [LiqunChen0606/Triangle-GAN](https://github.com/LiqunChen0606/Triangle-GAN) |\n| | CartoonGAN | CartoonGAN: Generative Adversarial Networks for Photo Cartoonization | CVPR 2018 | [thecvf](http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_CartoonGAN_Generative_Adversarial_CVPR_2018_paper.pdf) | [FlyingGoblin/CartoonGAN](https://github.com/FlyingGoblin/CartoonGAN), [unofficial test](https://github.com/Yijunmaverick/CartoonGAN-Test-Pytorch-Torch), [unofficial pytorch](https://github.com/znxlwm/pytorch-CartoonGAN) |\n| non-adversarial | NAM | NAM: Non-Adversarial Unsupervised Domain Mapping | ECCV 2018 | [1806.00804](https://arxiv.org/abs/1806.00804) | [facebookresearch/nam](https://github.com/facebookresearch/nam) |\n| | SCAN | Unsupervised Image-to-Image Translation with Stacked Cycle-Consistent Adversarial Networks | ECCV 2018 | [1807.08536](https://arxiv.org/abs/1807.08536) | |\n| dilated conv, improve shape deform. | GANimorph | Improved Shape Deformation in Unsupervised Image to Image Translation | ECCV 2018 | [1808.04325](https://arxiv.org/abs/1808.04325) | [brownvc/ganimorph](https://github.com/brownvc/ganimorph/) |\n| video | Recycle-GAN | Recycle-GAN: Unsupervised Video Retargeting | ECCV 2018 | [1808.05174](https://arxiv.org/abs/1808.05174) | [aayushbansal/Recycle-GAN](https://github.com/aayushbansal/Recycle-GAN) |\n| | OT-CycleGAN | Guiding the One-to-one Mapping in CycleGAN via Optimal Transport | AAAI 2019 | [1811.06284](https://arxiv.org/abs/1811.06284) | |\n| | Art2Real | Art2Real: Unfolding the Reality of Artworks via Semantically-Aware Image-to-Image Translation | CVPR 2019 | [1811.10666](https://arxiv.org/abs/1811.10666) | [aimagelab/art2real](https://github.com/aimagelab/art2real) |\n| latent cross-consistency | CrossNet | CrossNet: Latent Cross-Consistency for Unpaired Image Translation | WACV 2020 | [1901.04530](https://arxiv.org/abs/1901.04530) | |\n| | HarmonicGAN | Harmonic Unpaired Image-to-image Translation | ICLR 2019 | [1902.09727](https://arxiv.org/abs/1902.09727) | |\n| Person Generation | DG-Net | Joint Discriminative and Generative Learning for Person Re-identification | CVPR 2019 | [1904.07223](https://arxiv.org/abs/1904.07223) | [NVlabs/DG-Net](https://github.com/NVlabs/DG-Net) |\n| | TransGaGa | TransGaGa: Geometry-Aware Unsupervised Image-to-Image Translation | CVPR 2019 | [1904.09571](https://arxiv.org/abs/1904.09571) | [project](https://wywu.github.io/projects/TGaGa/TGaGa.html) |\n| | AGUIT | Attribute Guided Unpaired Image-to-Image Translation with Semi-supervised Learning | | [1904.12428](https://arxiv.org/abs/1904.12428) | [imlixinyang/AGUIT](https://github.com/imlixinyang/AGUIT) |\n| few shot | FUNIT | Few-Shot Unsupervised Image-to-Image Translation | ICCV 2019 | [1905.01723](https://arxiv.org/abs/1905.01723) | [project](https://nvlabs.github.io/FUNIT), [nvlabs/FUNIT](https://github.com/nvlabs/FUNIT/) |\n| zero shot | ZstGAN | ZstGAN: An Adversarial Approach for Unsupervised Zero-Shot Image-to-Image Translation | | [1906.00184](https://arxiv.org/abs/1906.00184) | [linjx-ustc1106/ZstGAN-PyTorch](https://github.com/linjx-ustc1106/ZstGAN-PyTorch) |\n| | | Cross-Domain Cascaded Deep Feature Translation | | [1906.01526](https://arxiv.org/abs/1906.01526) | |\n| | | Breaking the cycle -- Colleagues are all you need. | | [1911.10538](https://arxiv.org/abs/1911.10538) | |\n\n\n### Unsupervised - Attention/Instance guided\n\n| Note | Model | Paper | Conference | paper link | code link |\n| --------------------- | -------------------- | ------------------------------------------------------------ | ---------- | ------------------------------------------------------ | ------------------------------------------------------------ |\n| mask | ContrastGAN | Generative Semantic Manipulation with Mask-Contrasting GAN | ECCV 2018 | [1708.00315](https://arxiv.org/abs/1708.00315) | |\n| attention | DA-GAN | DA-GAN: Instance-level Image Translation by Deep Attention Generative Adversarial Networks | CVPR 2018 | [1802.06454](https://arxiv.org/abs/1802.06454) | |\n| mask / attention | Attention-GAN | Attention-GAN for Object Transfiguration in Wild Images | ECCV 2018 | [1803.06798](https://arxiv.org/abs/1803.06798) | |\n| attention | Attention guided GAN | Unsupervised Attention-guided Image to Image Translation | NIPS 2018 | [1806.02311](https://arxiv.org/abs/1806.02311) | [AlamiMejjati/Unsupervised-Attention-guided-Image-to-Image-Translation](https://github.com/AlamiMejjati/Unsupervised-Attention-guided-Image-to-Image-Translation) |\n| attention, one-sided | | Show, Attend and Translate: Unsupervised Image Translation with Self-Regularization and Attention | TIP 2019 | [1806.06195](https://arxiv.org/abs/1806.06195) | |\n| instance aware - mask | InstaGAN | Instance-aware image-to-image translation | ICLR 2019 | [openreview](https://openreview.net/pdf?id=ryxwJhC9YX) | [sangwoomo/instagan](https://github.com/sangwoomo/instagan) |\n| | AttentionGAN | Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation | IJCNN 2019 | [1903.12296](https://arxiv.org/abs/1903.12296) | [Ha0Tang/AttentionGAN](https://github.com/Ha0Tang/AttentionGAN) |\n| instance level - bbox | INIT | Towards Instance-level Image-to-Image Translation | CVPR 2019 | [1905.01744](https://arxiv.org/abs/1905.01744) | [project](http://zhiqiangshen.com/projects/INIT/index.html) |\n| mask guided | | Mask-Guided Portrait Editing with Conditional GANs | CVPR 2019 | [1905.10346](https://arxiv.org/abs/1905.10346) | [cientgu/Mask_Guided_Portrait_Editing](https://github.com/cientgu/Mask_Guided_Portrait_Editing) |\n| | U-GAT-IT | U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation | ICLR 2020 | [1907.10830](https://arxiv.org/abs/1907.10830) | [taki0112/UGATIT](https://github.com/taki0112/UGATIT), [znxlwm/UGATIT-pytorch](https://github.com/znxlwm/UGATIT-pytorch) |\n\n\n\n### Unsupervised - Many-to-many (Attributes)\n| Note | Model | Paper | Conference | paper link | code link |\n| ------------------------ | ---------------------------- | ------------------------------------------------------------ | ---------- | ---------------------------------------------- | ------------------------------------------------------------ |\n| | IcGAN | Invertible Conditional GANs for image editing | NIPSW 2016 | [1611.06355](https://arxiv.org/abs/1611.06355) | [Guim3/IcGAN](https://github.com/Guim3/IcGAN) |\n| | Conditional CycleGAN | Conditional CycleGAN for Attribute Guided Face Image Generation | ECCV 2018 | [1705.09966](https://arxiv.org/abs/1705.09966) | |\n| | StarGAN | StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation | CVPR 2018 | [1711.09020](https://arxiv.org/abs/1711.09020) | [yunjey/StarGAN](https://github.com/yunjey/StarGAN) |\n| | AttGAN | AttGAN: Facial Attribute Editing by Only Changing What You Want | TIP 2019 | [1711.10678](https://arxiv.org/abs/1711.10678) | [LynnHo/AttGAN-Tensorflow](https://github.com/LynnHo/AttGAN-Tensorflow) |\n| | ComboGAN | ComboGAN: Unrestrained Scalability for Image Domain Translation | CVPRW 2018 | [1712.06909](https://arxiv.org/abs/1712.06909) | [AAnoosheh/ComboGAN](https://github.com/AAnoosheh/ComboGAN) |\n| | AugCGAN (Augmented CycleGAN) | Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data | ICML 2018 | [1802.10151](https://arxiv.org/abs/1802.10151) | [aalmah/augmented_cyclegan](https://github.com/aalmah/augmented_cyclegan) |\n| | ModularGAN | Modular Generative Adversarial Networks | ECCV 2018 | [1804.03343](https://arxiv.org/abs/1804.03343) | |\n| sparsely grouped dataset | SG-GAN | Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation | MM 2018 | [1805.07509](https://arxiv.org/abs/1805.07509) | [zhangqianhui/Sparsely-Grouped-GAN](https://github.com/zhangqianhui/Sparsely-Grouped-GAN) |\n| | GANimation | GANimation: Anatomically-aware Facial Animation from a Single Image | ECCV 2018 | [1807.09251](https://arxiv.org/abs/1807.09251) | [albertpumarola/GANimation](https://github.com/albertpumarola/GANimation) |\n| | SingleGAN | SingleGAN: Image-to-Image Translation by a Single-Generator Network using Multiple Generative Adversarial Learning | ACCV 2018 | [1810.04991](https://arxiv.org/abs/1810.04991) | [Xiaoming-Yu/SingleGAN](https://github.com/Xiaoming-Yu/SingleGAN) |\n| | SMIT | SMIT: Stochastic Multi-Label Image-to-Image Translation | ICCVW 2019 | [1812.03704](https://arxiv.org/abs/1812.03704) | [BCV-Uniandes/SMIT](https://github.com/BCV-Uniandes/SMIT) |\n| | InjectionGAN | Toward Learning a Unified Many-to-Many Mapping for Diverse Image Translation | | [1905.08766](https://arxiv.org/abs/1905.08766) | |\n| | | Image-to-Image Translation with Multi-Path Consistency Regularization | IJCAI 2019 | [1905.12498](https://arxiv.org/abs/1905.12498) | |\n| | RelGAN | RelGAN: Multi-Domain Image-to-Image Translation via Relative Attributes | ICCV 2019 | [1908.07269](https://arxiv.org/abs/1908.07269) | [elvisyjlin/RelGAN-PyTorch](https://github.com/elvisyjlin/RelGAN-PyTorch), [willylulu/RelGAN](https://github.com/willylulu/RelGAN) |\n| | StarGAN v2 | StarGAN v2: Diverse Image Synthesis for Multiple Domains | CVPR 2020 | [1912.01865](https://arxiv.org/abs/1912.01865) | [clovaai/stargan-v2](https://github.com/clovaai/stargan-v2) |\n\n\n\n### Unsupervised - Disentangled (and/or Exemplar guided)\n\n| Note | Model | Paper | Conference | paper link | code link |\n| ----------------------------------- | ---------------------------- | ------------------------------------------------------------ | ---------- | ------------------------------------------------------------ | ------------------------------------------------------------ |\n| | XGAN | XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings | ICML 2018 | [1711.05139](https://arxiv.org/abs/1711.05139) | [dataset](https://google.github.io/cartoonset/) |\n| | ELEGANT | ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes | ECCV 2018 | [1803.10562](https://arxiv.org/abs/1803.10562) | [Prinsphield/ELEGANT](https://github.com/Prinsphield/ELEGANT) |\n| | MUNIT | Multimodal Unsupervised Image-to-Image Translation | ECCV 2018 | [1804.04732](https://arxiv.org/abs/1804.04732) | [NVlabs/MUNIT](https://github.com/NVlabs/MUNIT) |\n| | cd-GAN (Conditional DualGAN) | Conditional Image-to-Image Translation | CVPR 2018 | [1805.00251](https://arxiv.org/abs/1805.00251) | |\n| | EG-UNIT | Exemplar Guided Unsupervised Image-to-Image Translation | ICLR 2019 | [1805.11145](https://arxiv.org/abs/1805.11145) | |\n| | PairedCycleGAN | PairedCycleGAN: Asymmetric Style Transfer for Applying and Removing Makeup | CVPR 2018 | [thecvf](http://openaccess.thecvf.com/content_cvpr_2018/papers/Chang_PairedCycleGAN_Asymmetric_Style_CVPR_2018_paper.pdf) | |\n| | DRIT | Diverse Image-to-Image Translation via Disentangled Representations | ECCV 2018 | [1808.00948](https://arxiv.org/abs/1808.00948) | [HsinYingLee/DRIT](https://github.com/HsinYingLee/DRIT) |\n| | UFDN | A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation | NIPS 2018 | [1809.01361](https://arxiv.org/abs/1809.01361) | [Alexander-H-Liu/UFDN](https://github.com/Alexander-H-Liu/UFDN) |\n| non-disentangle, face makeup guided | BeautyGAN | BeautyGAN: Instance-level Facial Makeup Transfer with Deep Generative Adversarial Network | MM 2018 | [author](https://liusi-group.com/pdf/BeautyGAN-camera-ready.pdf) | [wtjiang98/BeautyGAN_pytorch](https://github.com/wtjiang98/BeautyGAN_pytorch) |\n| | GDWCT | Image-to-Image Translation via Group-wise Deep Whitening and Coloring Transformation | CVPR 2019 | [1812.09912](https://arxiv.org/abs/1812.09912) | [WonwoongCho/GDWCT](https://github.com/WonwoongCho/GDWCT) |\n| | DRIT++ | DRIT++: Diverse Image-to-Image Translation via Disentangled Representations | IJCV 2020 | [1905.01270](https://arxiv.org/abs/1905.01270) | [project](http://vllab.ucmerced.edu/hylee/DRIT_pp/), [HsinYingLee/MDMM](https://github.com/HsinYingLee/MDMM) |\n| | HiDT | High-Resolution Daytime Translation Without Domain Labels | CVPR 2020 | [2003.08791](https://arxiv.org/abs/2003.08791) | [project](https://saic-mdal.github.io/HiDT/), [saic-mdal/HiDT](https://github.com/saic-mdal/HiDT) |\n\n\n\n## To be classified\n\n* EDIT: Exemplar-Domain Aware Image-to-Image Translation. https://arxiv.org/abs/1911.10520\n\n* Multi-mapping Image-to-Image Translation via Learning Disentanglement. https://arxiv.org/abs/1909.07877. NeurIPS 2019. https://github.com/Xiaoming-Yu/DMIT\n\n* GANHOPPER: Multi-Hop GAN for Unsupervised Image-to-Image Translation. https://arxiv.org/abs/2002.10102\n\n* GMM-UNIT: Unsupervised Multi-Domain and Multi-Modal Image-to-Image Translation via Attribute Gaussian Mixture Modeling. https://arxiv.org/abs/2003.06788.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flzhbrian%2Fimage-to-image-papers","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flzhbrian%2Fimage-to-image-papers","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flzhbrian%2Fimage-to-image-papers/lists"}