• Image Synthesis
• Image Translation
• Image Manipulation
• Style Transfer

Image Synthesis

• Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
  Keyu Tian, Yi Jiang, Zehuan Yuan, Bingyue Peng, Liwei Wang
  [NeurIPS 2024 Oral] [Pytorch-Code]
  [VAR] 🔥

• Adding Conditional Control to Text-to-Image Diffusion Models
  Lvmin Zhang, Anyi Rao, Maneesh Agrawala
  [ICCV 2023 Oral] [Project] [Pytorch-Code]
  [ControlNet] 🔥

• Disentangling Writer and Character Styles for Handwriting Generation
  Shilin Lu, Yanzhu Liu, Adams Wai-Kin Kong
  [ICCV 2023] [Project] [Pytorch-Code]
  

• Disentangling Writer and Character Styles for Handwriting Generation
  Gang Dai, Yifan Zhang, Qingfeng Wang, Qing Du, Zhuliang Yu, Zhuoman Liu, Shuangping Huang [CVPR 2023] [Pytorch-Code]
  

• All are Worth Words: A ViT Backbone for Diffusion Models
  Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, Jun Zhu
  [CVPR 2023] [Pytorch-Code]
  

• Wavelet Diffusion Models are fast and scalable Image Generators
  Hao Phung, Quan Dao, Anh Tran
  [CVPR 2023] [Pytorch-Code]
  

• LayoutDiffusion: Controllable Diffusion Model for Layout-to-image Generation
  Guangcong Zheng, Xianpan Zhou, Xuewei Li, Zhongang Qi, Ying Shan, Xi Li
  [CVPR 2023] [Pytorch-Code]
  

• MAGE: MAsked Generative Encoder to Unify Representation Learning and Image Synthesis
  Tianhong Li, Huiwen Chang, Shlok Kumar Mishra, Han Zhang, Dina Katabi, Dilip Krishnan
  [CVPR 2023] [Pytorch-Code]
  

• Person Image Synthesis via Denoising Diffusion Model
  Ankan Kumar Bhunia, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Jorma Laaksonen, Mubarak Shah, Fahad Shahbaz Khan
  [CVPR 2023] [Project] [Pytorch-Code]
  

• Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation
  Narek Tumanyan, Michal Geyer, Shai Bagon, Tali Dekel
  [CVPR 2023] [Project] [Pytorch-Code]
  

• Collaborative Diffusion for Multi-Modal Face Generation and Editing
  Ziqi Huang, Kelvin C.K. Chan, Yuming Jiang, Ziwei Liu
  [CVPR 2023] [Project] [Pytorch-Code]
  

• DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation
  Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, Kfir Aberman
  [CVPR 2023] [Project]
  Google

• Custom Diffusion: Multi-Concept Customization of Text-to-Image Diffusion
  Nupur Kumari, Bingliang Zhang, Richard Zhang, Eli Shechtman, Jun-Yan Zhu
  [CVPR 2023] [Project] [Pytorch-Code]
  🔥

• GLIGEN: Open-Set Grounded Text-to-Image Generation
  Yuheng Li, Haotian Liu, Qingyang Wu, Fangzhou Mu, Jianwei Yang, Jianfeng Gao, Chunyuan Li, Yong Jae Lee
  [CVPR 2023] [Project] [Pytorch-Code]
  🔥

• Scaling up GANs for Text-to-Image Synthesis
  Minguk Kang, Jun-Yan Zhu, Richard Zhang, Jaesik Park, Eli Shechtman, Sylvain Paris, Taesung Park
  [CVPR 2023] [Project]
  Adobe

• GALIP: Generative Adversarial CLIPs for Text-to-Image Synthesis
  Ming Tao, Bing-Kun Bao, Hao Tang, Changsheng Xu
  [CVPR 2023] [Pytorch-Code]
  

• Multimodal Image Synthesis and Editing: A Survey
  Fangneng Zhan, Yingchen Yu, Rongliang Wu, Jiahui Zhang, Shijian Lu, Lingjie Liu, Adam Kortylewsk, Christian Theobalt, Eric Xing
  [arXiv 2112] [Pytorch-Code]

• High-Resolution Image Synthesis with Latent Diffusion Models
  Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer
  [CVPR 2022 Oral] [Project] [Pytorch-Code]
  [Stable Difussion] 🔥

• LAFITE: Towards Language-Free Training for Text-to-Image Generation
  Yufan Zhou, Ruiyi Zhang, Changyou Chen, Chunyuan Li, Chris Tensmeyer, Tong Yu, Jiuxiang Gu, Jinhui Xu, Tong Sun
  [CVPR 2022] [Pytorch-Code]
  

• Text to Image Generation with Semantic-Spatial Aware GAN
  Kai Hu, Wentong Liao, Michael Ying Yang, Bodo Rosenhahn
  [CVPR 2022] [Pytorch-Code]
  

• HyperInverter: Improving StyleGAN Inversion via Hypernetwork
  Tan M. Dinh, Anh Tran, Rang Nguyen, Binh-Son Hua
  [CVPR 2022] [Pytorch-Code]
  

• NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images
  Ben Mildenhall, Peter Hedman, Ricardo Martin-Brualla, Pratul Srinivasan, Jonathan Barron
  [CVPR 2022 Oral] [Project] [TF-Code]

• Im2Vec Synthesizing Vector Graphics without Vector Supervision
  Sheng-Yu Wang, David Bau, Jun-Yan Zhu
  [ICCV 2021] [Project] [Pytorch-Code]
  

• Im2Vec Synthesizing Vector Graphics without Vector Supervision
  Pradyumna Reddy, Michael Gharbi, Michal Lukac, Niloy J. Mitra
  [CVPR 2021 Oral] [Project] [Pytorch-Code]
  

• Image Generators with Conditionally-Independent Pixel Synthesis
  Ivan Anokhin, Kirill Demochkin, Taras Khakhulin, Gleb Sterkin, Victor Lempitsky, Denis Korzhenkov
  [CVPR 2021 Oral] [Pytorch-Code]
  [CIPS]

• Anycost GANs for Interactive Image Synthesis and Editing
  Ji Lin, Richard Zhang, Frieder Ganz, Song Han, Jun-Yan Zhu
  [CVPR 2021] [Project] [Pytorch-Code]
  

• Generative Hierarchical Features from Synthesizing Images
  Yinghao Xu, Yujun Shen, Jiapeng Zhu, Ceyuan Yang, Bolei Zhou
  [CVPR 2021 Oral] [Project] [TF-Code]
  [GH-Feat]

• SEAN: Image Synthesis with Semantic Region-Adaptive Normalization
  Peihao Zhu, Rameen Abdal, Yipeng Qin, Peter Wonka
  [CVPR 2020 Oral] [Project] [PyTorch-Code]
  [★] 与SPADE类似, 不过提取了每个语义类别的style信息, 并与语义mask一起, 通过AdaIN的形式控制生成器的activation

• A U-Net Based Discriminator for Generative Adversarial Networks
  Edgar Sch ̈onfeld, Bernt Schiele
  [CVPR 2020] [PyTorch-Code]
  [★] 采用UNet形式的判别器, 可以用CutMix等数据增强策略

• Semantic Pyramid for Image Generation
  Assaf Shocher, Yossi Gandelsman, Inbar Mosseri, Michal Yarom, Michal Irani, William T. Freeman, Tali Dekel
  [CVPR 2020 Oral] [Project]
  [★☆] 提出一个通用的图像生成框架, 将预训练的多尺度特征融合进生成网络中, 实现对不同语义层次图像内容的生成控制, 融合时使用mask选择特定区域的生成. 论文思路简单清晰, 适用于多种任务

• Few-shot Image Generation with Elastic Weight Consolidation
  Yijun Li, Richard Zhang, Jingwan Lu, Eli Shechtman
  [NeurIPS 2020] [Project]
  

• Analyzing and Improving the Image Quality of StyleGAN
  Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, Timo Aila
  [CVPR 2020] [TF-Code]
  [★☆] 对StyleGAN做了一些改进, 如去掉减均值操作

• Training Generative Adversarial Networks with Limited Data
  Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, Timo Aila
  [arXiv 2006] [TF-Code] [Pytorch-Code]
  [StyleGAN2-ADA]

• Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis
  Xihui Liu, Guojun Yin, Jing Shao, Xiaogang Wang, Hongsheng Li
  [NIPS 2019] [Pytorch-Code]
  [CC-FPSE] [★] 使用分割图通过一个网络预测每个pixel我的卷积参数, 虽然使用的depthwise卷积, 但参数量仍然非常庞大, 所以生成的图像较小.

• Large Scale GAN Training for High Fidelity Natural Image Synthesis
  Andrew Brock, Jeff Donahue, Karen Simonyan
  [ICLR 2019] [Pytorch-Code]
  [BigGAN] [★☆] 加大网络复杂度和训练batch size, 另外使用了截断等一些trick, 大幅提升了生成效果

• SinGAN: Learning a generative model from a single natural image
  Tamar Rott Shaham, Tali Dekel, Tomer Michaeli
  [ICCV 2019 Best Paper] [Project] [Pytorch-Code]
  

• Dynamic-Net: Tuning the Objective Without Re-training for Synthesis Tasks
  Alon Shoshan, Roey Mechrez, Lihi Zelnik-Manor
  [ICCV 2019] [Project] [PyTorch-Code]
  [★☆] 先以Objective 0训练主干网络, 之后固定主干网络以Objective 1训练tuning block. 测试时手动指定插值系数, 达到在O0和O1之间的输出效果. 论文思路和实现都很简单, 分析论述方式值得学习

• Semantic Image Synthesis with Spatially-Adaptive Normalization
  Taesung Park, Ming-Yu Liu, Ting-Chun Wang, Jun-Yan Zhu
  [CVPR 2019 Oral] [Project] [PyTorch-Code]
  [SPADE] [★★] 提出一个与AdaIN结构相似的SPADE模块, 用于将语义信息嵌入到生成模型中

• A Style-Based Generator Architecture for Generative Adversarial Networks
  Tero Karras, Samuli Laine, Timo Aila
  [CVPR 2019] [TF-Code]
  [StyleGAN] [★★☆] 通过若干FC层得到中间向量, 使用AdaIn的形式将中间向量作用在生成器上

• Image Generation From Layout
  Bo Zhao, Lili Meng, Weidong Yin, Leonid Sigal
  [CVPR 2019 Oral] [Pytorch-Code]
  

• Progressive Growing of GANs for Improved Quality, Stability, and Variation
  Qifeng Chen, Vladlen Koltun
  [CVPR 2018] [Project] [TF-Code]
  

• High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
  Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Andrew Tao, Jan Kautz, Bryan Catanzaro
  [CVPR 2018] [Project] [Pytorch-Code]
  [pix2pixHD] [★☆] 生成2048 x 1024的高清图像, 提出了一个local到global的多尺度生成器结构, 使用多尺度的判别器, 提出使用判别器的中间层feature相似度作为一个loss

• Progressive Growing of GANs for Improved Quality, Stability, and Variation
  Tero Karras, Timo Aila, Samuli Laine, Jaakko Lehtinen
  [ICLR 2018] [Pytorch-Code]
  [★★☆]

Image Translation

• MonoPix - Contrastive Monotonic Pixel-Level Modulation
  Kun Lu, Rongpeng Li, Honggang Zhang
  [ECCV 2022 Oral] [Pytorch-Code]
  

• Drop the GAN: In Defense of Patches Nearest Neighbors as Single Image Generative Model
  Niv Granot, Ben Feinstein, Assaf Shocher, Shai Bagon, Michal Irani
  [CVPR 2022] [Project] [Pytorch-Code]
  

• Day-to-Night Image Synthesis for Training Nighttime Neural ISPs
  Abhijith Punnappurath, Abdullah Abuolaim, Abdelrahman Abdelhamed, Alex Levinshtein, Michael S. Brown
  [CVPR 2022 Oral] [Code]
  

• Neural Texture Extraction and Distribution for Controllable Person Image Synthesis
  Yurui Ren, Xiaoqing Fan, Ge Li, Shan Liu, Thomas H. Li
  [CVPR 2022 Oral] [Pytorch-Code]
  

• Exploring Patch-wise Semantic Relation for Contrastive Learning in Image-to-Image Translation Tasks
  Chanyong Jung, Gihyun Kwon, Jong Chul Ye
  [CVPR 2022] [Pytorch-Code]
  

• Unsupervised Image-to-Image Translation with Generative Prior
  Shuai Yang, Liming Jiang, Ziwei Liu, Chen Change Loy
  [CVPR 2022] [Project] [Pytorch-Code]
  [GP-UNIT]

• Marginal Contrastive Correspondence for Guided Image Generation
  Fangneng Zhan, Yingchen Yu, Rongliang Wu, Jiahui Zhang, Shijian Lu, Changgong Zhang
  [CVPR 2022] [Pytorch-Code]
  

• QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translation
  Xueqi Hu, Xinyue Zhou, Qiusheng Huang, Zhengyi Shi, Li Sun, Qingli Li
  [CVPR 2022] [Pytorch-Code]
  

• A Style-aware Discriminator for Controllable Image Translation
  Kunhee Kim, Sanghun Park, Eunyeong Jeon, Taehun Kim, Daijin Kim
  [CVPR 2022] [Pytorch-Code]
  

• Maximum Spatial Perturbation for Image-to-Image Translation
  Yanwu Xu, Shaoan Xie, Wenhao Wu, Kun Zhang, Mingming Gong, Kayhan Batmanghelich
  [CVPR 2022] [Pytorch-Code]
  [MSPC]

• AgileGAN: Stylizing Portraits by Inversion-Consistent Transfer Learning
  Guoxian Song, Linjie Luo, Jing Liu, Wan-Chun Ma, Chunpong Lai, Chuanxia Zheng, Tat-Jen Cham
  [SIGGRAPH 2021] [Project]
  

• The Spatially-Correlative Loss for Various Image Translation Tasks
  Chuanxia Zheng, Tat-Jen Cham, Jianfei Cai
  [CVPR 2021] [Project] [Pytorch-Code]
  

• Unpaired Image-to-Image Translation via Latent Energy Transport
  Yang Zhao, Jianwen Xie, Ping Li
  [CVPR 2021] [Pytorch-Code]
  

• CoCosNet v2: Full-Resolution Correspondence Learning for Image Translation
  Xingran Zhou, Bo Zhang, Ting Zhang, Pan Zhang, Jianmin Bao, Dong Chen, Zhongfei Zhang, Fang Wen
  [CVPR 2021 Oral] [Pytorch-Code]
  

• High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network
  Tamar Rott Shaham, Michaël Gharbi, Richard Zhang, Eli Shechtman, Tomer Michaeli
  [CVPR 2021] [Pytorch-Code]
  [LPTN] [★] 把大多数计算放在laplacian金字塔低分辨率层

• Spatially-Adaptive Pixelwise Networks for Fast Image Translation
  Tamar Rott Shaham, Michaël Gharbi, Richard Zhang, Eli Shechtman, Tomer Michaeli
  [CVPR 2021] [Project]
  [ASAPNet] [★★] (快速图像生成) 在小分辨率(降32倍)上预测每个pixel的MLP映射系数, 将位置信息编码为不同频率的sin, cosine信息, 提升了生成图像的细节. 论文很有实际意义, 值得学习.

• Few-shot Image Generation via Cross-domain Correspondence
  Utkarsh Ojha , Yijun Li, Jingwan Lu, Alexei A. Efros, Yong Jae Lee, Eli Shechtman, Richard Zhang
  [CVPR 2021] [Project] [Pytorch-Code]
  

• Unsupervised Image Transformation Learning via Generative Adversarial Networks
  Kaiwen Zha, Yujun Shen, Bolei Zhou
  [arXiv 2103] [Project]
  [TrGAN]

• COCO-FUNIT: Few-Shot Unsupervised Image Translation with a Content Conditioned Style Encoder
  Kuniaki Saito, Kate Saenko, Ming-Yu Liu
  [ECCV 2020] [Pytorch-Code]
  

• Contrastive Learning for Unpaired Image-to-Image Translation
  Taesung Park, Alexei A. Efros, Richard Zhang, Jun-Yan Zhu
  [ECCV 2020] [Project] [Pytorch-Code]
  [CUT]

• DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs
  Yaxing Wang, Lu Yu, Joost van de Weijer
  [NeurIPS 2020] [Pytorch-Code]
  

• Mask Based Unsupervised Content Transfer
  Ron Mokady, Sagie Benaim, Lior Wolf, Amit Bermano
  [ICLR 2020] [Pytorch-Code]
  

• STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing
  Ming Liu, Yukang Ding, Min Xia, Xiao Liu, Errui Ding, Wangmeng Zuo, Shilei Wen
  [CVPR 2019] [Pytorch-Code]
  

• RelGAN: Multi-Domain Image-to-Image Translation via Relative Attributes
  Po-Wei Wu, Yu-Jing Lin, Che-Han Chang, Edward Y. Chang, Shih-Wei Liao
  [Pytorch-Code]
  

• Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer
  Ori Press, Tomer Galanti, Sagie Benaim, Lior Wolf
  [ICLR 2019] [Pytorch-Code]
  

• StarGAN v2: Diverse Image Synthesis for Multiple Domains
  Yunjey Choi, Youngjung Uh, Jaejun Yoo, Jung-Woo Ha
  [CVPR 2020] [Pytorch-Code]
  

• StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
  Yunjey Choi, Minje Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, Jaegul Choo
  [CVPR 2018 Oral] [Pytorch-Code]
  

• Multimodal Unsupervised Image-to-image Translation
  Xun Huang, Ming-Yu Liu, Serge Belongie, Jan Kautz
  [ECCV 2018] [Pytorch-Code]
  [MUNIT]

• DRIT++: Diverse Image-to-Image Translation via Disentangled Representations
  Hsin-Ying Lee, Hung-Yu Tseng, Qi Mao, Jia-Bin Huang, Yu-Ding Lu, Maneesh Kumar Singh, Ming-Hsuan Yang
  [IJCV 2020] [Pytorch-Code]
  

• Diverse Image-to-Image Translation via Disentangled Representations
  Hsin-Ying Lee, Hung-Yu Tseng, Jia-Bin Huang, Maneesh Kumar Singh, Ming-Hsuan Yang
  [ECCV 2018 Oral]
  [DRIT]

• Image-to-Image Translation with Conditional Adversarial Nets
  Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros
  [CVPR 2017] [Project]
  [pix2pix] [★★☆] 使用patchGAN的图像迁移的经典方法

• Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
  Jun-Yan Zhu, Taesung Park, Phillip Isola, Alexei A. Efros
  [ICCV 2017] [Project]
  [CycleGAN] [★★★] 无监督图像迁移

• Toward Multimodal Image-to-Image Translation
  Jun-Yan Zhu, Richard Zhang, Deepak Pathak, Trevor Darrell, Alexei A. Efros, Oliver Wang, Eli Shechtman
  [NIPS 2017] [Pytorch-Code]
  [BicycleGAN]

• Unsupervised image-to-image translation networks
  Ming-Yu Liu, Thomas Breuel, Jan Kautz
  [NIPS 2017] [Pytorch-Code]
  [UNIT]

Image Manipulation

• EDICT: Exact Diffusion Inversion via Coupled Transformations
  Qinghe Wang, Xu Jia, Xiaomin Li, Taiqing Li, Liqian Ma, Yunzhi Zhuge, Huchuan Lu
  [arXiv 2401] [Project] [Pytorch-Code]
  

• HairCLIPv2: Unifying Hair Editing via Proxy Feature Blending
  Tianyi Wei, Dongdong Chen, Wenbo Zhou, Jing Liao, Weiming Zhang, Gang Hua, Nenghai Yu
  [ICCV 2023] [Pytorch-Code]
  

• MasaCtrl: Tuning-free Mutual Self-Attention Control for Consistent Image Synthesis and Editing
  Mingdeng Cao, Xintao Wang, Zhongang Qi, Ying Shan, Xiaohu Qie, Yinqiang Zheng
  [ICCV 2023] [Pytorch-Code]
  

• Multimodal Garment Designer: Human-Centric Latent Diffusion Models for Fashion Image Editing
  Alberto Baldrati, Davide Morelli, Giuseppe Cartella, Marcella Cornia, Marco Bertini, Rita Cucchiara
  [ICCV 2023] [Pytorch-Code]
  

• EDICT: Exact Diffusion Inversion via Coupled Transformations
  Bram Wallace, Akash Gokul, Nikhil Naik
  [CVPR 2023] [Pytorch-Code]
  

• DeltaEdit: Exploring Text-free Training for Text-Driven Image Manipulation
  Yueming Lyu, Tianwei Lin, Fu Li, Dongliang He, Jing Dong, Tieniu Tan
  [CVPR 2023] [Pytorch-Code]
  

• InstructPix2Pix: Learning to Follow Image Editing Instructions
  Tim Brooks, Aleksander Holynski, Alexei A. Efros
  [CVPR 2023] [Project] [Pytorch-Code]
  🔥

• Delving StyleGAN Inversion for Image Editing: A Foundation Latent Space Viewpoint
  Hongyu Liu, Yibing Song, Qifeng Chen>
  [CVPR 2023] [Project] [Pytorch-Code]
  

• StyleRes: Transforming the Residuals for Real Image Editing With StyleGAN
  Hamza Pehlivan, Yusuf Dalva, Aysegul Dundar
  [CVPR 2023] [Project] [Pytorch-Code]
  

• Prompt-to-Prompt Image Editing with Cross-Attention Control
  Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, Daniel Cohen-Or
  [ICLR 2023] [Project] [Pytorch-Code]
  🔥

• Null-text Inversion for Editing Real Images using Guided Diffusion Models
  Ron Mokady, Amir Hertz, Kfir Aberman, Yael Pritch, Daniel Cohen-Or
  [CVPR 2023] [Project] [Pytorch-Code]
  🔥

• Paint by Example: Exemplar-based Image Editing with Diffusion Models
  Binxin Yang, Shuyang Gu, Bo Zhang, Ting Zhang, Xuejin Chen, Xiaoyan Sun, Dong Chen, Fang Wen
  [CVPR 2023] [Pytorch-Code]
  

• DiffusionRig: Learning Personalized Priors for Facial Appearance Editing
  Zheng Ding, Xuaner Zhang, Zhihao Xia, Lars Jebe Zhuowen Tu, Xiuming Zhang
  [CVPR 2023] [Project] [Pytorch-Code]
  

• SINgle Image Editing with Text-to-Image Diffusion Models
  Zhixing Zhang, Ligong Han, Arnab Ghosh, Dimitris Metaxas, Jian Ren
  [CVPR 2023] [Project] [Pytorch-Code]
  [SINE]

• Spatially-Adaptive Multilayer Selection for GAN Inversion and Editing
  Gaurav Parmar, Yijun Li, Jingwan Lu, Richard Zhang, Jun-Yan Zhu, Krishna Kumar Singh
  [CVPR 2022] [Project] [Pytorch-Code]
  

• SketchEdit: Mask-Free Local Image Manipulation with Partial Sketches
  Yu Zeng, Zhe Lin, Vishal M. Patel
  [CVPR 2022] [Project] [Pytorch-Code]
  

• HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing
  Yuval Alaluf, Omer Tov, Ron Mokady, Rinon Gal, Amit H. Bermano
  [CVPR 2022] [Project] [Pytorch-Code]
  

• High-Fidelity GAN Inversion for Image Attribute Editing
  Tengfei Wang, Yong Zhang, Yanbo Fan, Jue Wang, Qifeng Chen
  [CVPR 2022] [Project] [Pytorch-Code]
  [HFGI]

• Blended Diffusion for Text-driven Editing of Natural Images
  Omri Avrahami, Dani Lischinski, Ohad Fried
  [CVPR 2022] [Pytorch-Code]
  

• Style Transformer for Image Inversion and Editing
  Xueqi Hu, Qiusheng Huang, Zhengyi Shi, Siyuan Li, Changxin Gao, Li Sun, Qingli Li
  [CVPR 2022] [Pytorch-Code]
  

• DeFLOCNet: Deep Image Editing via Flexible Low-level Controls
  Hongyu Liu, Ziyu Wan, Wei Huang, Yibing Song, Xintong Han , Jing Liao, Bin Jiang, Wei Liu
  [CVPR 2021] [Pytorch-Code]
  

• HiSD: Image-to-image Translation via Hierarchical Style Disentanglement
  Xinyang Li, Shengchuan Zhang, Jie Hu, Liujuan Cao, Xiaopeng Hong, Xudong Mao, Feiyue Huang, Yongjian Wu, Rongrong Ji
  [CVPR 2021 Oral] [Pytorch-Code]
  [★★] 大致浏览, 可将人脸各属性解耦, 并实现准确控制. 将CelebA的属性信息进一步划分为相互独立的tags(如刘海, 眼镜)和互斥的attributes(如黑发和棕发),并为每个tag和attribute分别构造模块. 论文整体思路很清晰, 代码也很友好, 可以仔细研究一下.

• Closed-Form Factorization of Latent Semantics in GANs
  Yujun Shen, Bolei Zhou
  [CVPR 2021 Oral] [Project] [Pytorch-Code]
  [SeFa]

• IDInvert：In-Domain GAN Inversion for Real Image Editing
  Jiapeng Zhu, Yujun Shen, Deli Zhao, Bolei Zhou
  [ECCV 2020] [Project] [TF-Code]
  

• Collaborative Learning for Faster StyleGAN Embedding
  Shanyan Guan, Ying Tai, Bingbing Ni, Feida Zhu, Feiyue Huang, Xiaokang Yang
  [arXiv 2007]
  [★] (latent code embedding)

• Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?
  Rameen Abdal, Yipeng Qin, Peter Wonka
  [ICCV 2019] [TF-Code]
  [★☆] (latent code embedding)

Style Transfer

• Neural-based 3D Lookup Tables for Video Photorealistic Style Transfer
  Yaosen Chen, Han Yang, Yuexin Yang, Yuegen Liu, Wei Wang, Xuming Wen, Chaoping Xie
  [arXiv 2303] [Pytorch-Code]
  [★] [NLUT]

• Inversion-Based Style Transfer with Diffusion Models
  Yuxin Zhang, Nisha Huang, Fan Tang, Haibin Huang, Chongyang Ma, Weiming Dong, Changsheng Xu
  [CVPR 2023] [Pytorch-Code]
  [InST]

• QuantArt: Quantizing Image Style Transfer Towards High Visual Fidelity
  Siyu Huang, Jie An, Donglai Wei, Jiebo Luo, Hanspeter Pfister
  [CVPR 2023] [Pytorch-Code]
  

• Fix the Noise: Disentangling Source Feature for Controllable Domain Translation
  Dongyeun Lee, Jae Young Lee, Doyeon Kim, Jaehyun Choi, Jaejun Yoo, Junmo Kim
  [CVPR 2023] [Pytorch-Code]
  

• Neural Preset for Color Style Transfer
  Zhanghan Ke, Yuhao Liu, Lei Zhu, Nanxuan Zhao, Rynson W.H. Lau
  [CVPR 2023] [Pytorch-Code]
  [NeuralPreset]

• Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer
  Shuai Yang, Liming Jiang, Ziwei Liu, Chen Change Loy
  [CVPR 2021] [Pytorch-Code]
  [DualStyleGAN]

• Industrial Style Transfer with Large-scale Geometric Warping and Content Preservation
  Jinchao Yang, Fei Guo, Shuo Chen, Jun Li, Jian Yang
  [CVPR 2021] [Pytorch-Code]
  [InST]

• Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization
  Yabin Zhang, Minghan Li, Ruihuang Li, Kui Jia, Lei Zhang
  [CVPR 2021 Oral] [Pytorch-Code]
  

• Style-ERD: Responsive and Coherent Online Motion Style Transfer
  Tianxin Tao, Xiaohang Zhan, Zhongquan Chen, Michiel van de Panne
  [CVPR 2021] [Pytorch-Code]
  

• CLIPstyler:Image Style Transfer with a Single Text Condition
  Gihyun Kwon, Jong Chul Ye
  [CVPR 2021] [Pytorch-Code]
  

• Rethinking and Improving the Robustness of Image Style Transfer
  Pei Wang, Yijun Li, Nuno Vasconcelos
  [CVPR 2021 Oral] [Pytorch-Code]
  [SWAG] [★] 实验说明resnet等网络使用的shotcut结构会产生大激活值和小的层间entropy, 不利于基于gram矩阵的loss计算. 因此提出了用softmax平滑激活值, 再用来计算loss.

• Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfer
  Xide Xia, Meng Zhang, Tianfan Xue, Zheng Sun, Hui Fang, Brian Kulis, Jiawen Chen
  [ECCV 2020] [Unoffical-Pytorch-Code]
  [★★] 基于HDRNet的实时风格迁移, 创新点尽管不是很多, 但是工作很有价值