mlsanityData to 7 Oct 2026

In-Domain GAN Inversion for Real Image Editing

arXiv 2004.00049 ↗First posted 31 Mar 2020cs.CVcited by 420

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Cited by 55 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Recent work has shown that a variety of semantics emerge in the latent space of Generative Adversarial Networks (GANs) when being trained to synthesize images. However, it is difficult to use these learned semantics for real image editing. A common practice of feeding a real image to a trained GAN generator is to invert it back to a latent code. However, existing inversion methods typically focus on reconstructing the target image by pixel values yet fail to land the inverted code in the semantic domain of the original latent space. As a result, the reconstructed image cannot well support semantic editing through varying the inverted code. To solve this problem, we propose an in-domain GAN inversion approach, which not only faithfully reconstructs the input image but also ensures the inverted code to be semantically meaningful for editing. We first learn a novel domain-guided encoder to project a given image to the native latent space of GANs. We then propose domain-regularized optimization by involving the encoder as a regularizer to fine-tune the code produced by the encoder and better recover the target image. Extensive experiments suggest that our inversion method achieves satisfying real image reconstruction and more importantly facilitates various image editing tasks, significantly outperforming start-of-the-arts.

Key facts. Cited by 420 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Mar 2020.

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Citing papers per year: 2021: 100; 2022: 129; 2023: 115; 2024: 53; 2025: 22; 2026: 1.

420 papers.

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  1. 2022Prompt-to-Prompt Image Editing with Cross Attention Controlcited by 2,409
  2. 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,231
  3. 2021SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equationscited by 1,903
  4. 2021Blended Diffusion for Text-driven Editing of Natural Imagescited by 924
  5. 2022DiffEdit: Diffusion-based semantic image editing with mask guidancecited by 643
  6. 2021DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulationcited by 640
  7. 2021ILVR: Conditioning Method for Denoising Diffusion Probabilistic Modelscited by 634
  8. 2022Compositional Visual Generation with Composable Diffusion Modelscited by 619
  9. 2021Designing an Encoder for StyleGAN Image Manipulationcited by 542
  10. 2023Zero-shot Image-to-Image Translationcited by 503
  11. 2022StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasetscited by 489
  12. 2022Blended Latent Diffusioncited by 430
38 more
  1. 2021Pivotal Tuning for Latent-based Editing of Real Imagescited by 427
  2. 2021GAN Inversion: A Surveycited by 366
  3. 2021Towards Real-World Blind Face Restoration with Generative Facial Priorcited by 347
  4. 2021ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinementcited by 248
  5. 2023Break-A-Scene: Extracting Multiple Concepts from a Single Imagecited by 230
  6. 2021More Control for Free! Image Synthesis with Semantic Diffusion Guidancecited by 211
  7. 2021HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editingcited by 208
  8. 2022Re-Imagen: Retrieval-Augmented Text-to-Image Generatorcited by 208
  9. 2023Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Modelscited by 203
  10. 2021High-Fidelity GAN Inversion for Image Attribute Editingcited by 197
  11. 2022Latent Image Animator: Learning to Animate Images via Latent Space Navigationcited by 172
  12. 2023Zero-Shot Composed Image Retrieval with Textual Inversioncited by 171
  13. 2022StyleHEAT: One-Shot High-Resolution Editable Talking Face Generation via Pre-trained StyleGANcited by 169
  14. 2021A Good Image Generator Is What You Need for High-Resolution Video Synthesiscited by 162
  15. 2022SINE: SINgle Image Editing with Text-to-Image Diffusion Modelscited by 159
  16. 2021EditGAN: High-Precision Semantic Image Editingcited by 156
  17. 2021Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalizationcited by 141
  18. 2021Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesiscited by 140
  19. 2024ReNoise: Real Image Inversion Through Iterative Noisingcited by 127
  20. 2023Dataset Distillation: A Comprehensive Reviewcited by 126
  21. 2023Generalizing Dataset Distillation via Deep Generative Priorcited by 125
  22. 2024Is Sora a World Simulator? A Comprehensive Survey on General World Models and Beyondcited by 111
  23. 2024Stable Flow: Vital Layers for Training-Free Image Editingcited by 111
  24. 20213D-aware Image Synthesis via Learning Structural and Textural Representationscited by 109
  25. 2021Paint by Wordcited by 107
  26. 2021One Shot Face Swapping on Megapixelscited by 105
  27. 2021Gradient Inversion with Generative Image Priorcited by 101
  28. 2021D2C: Diffusion-Denoising Models for Few-shot Conditional Generationcited by 96
  29. 2021Only a Matter of Style: Age Transformation Using a Style-Based Regression Modelcited by 92
  30. 2021GAN-Control: Explicitly Controllable GANscited by 91
  31. 2021Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editingcited by 91
  32. 2021HyperInverter: Improving StyleGAN Inversion via Hypernetworkcited by 85
  33. 2023Domain-Agnostic Tuning-Encoder for Fast Personalization of Text-To-Image Modelscited by 85
  34. 2024Arbitrary-steps Image Super-resolution via Diffusion Inversioncited by 85
  35. 2023A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?cited by 84
  36. 2021Constrained Graphic Layout Generation via Latent Optimizationcited by 83
  37. 2021CLIP2StyleGAN: Unsupervised Extraction of StyleGAN Edit Directionscited by 78
  38. 2022Synthesizing Informative Training Samples with GANcited by 77

50 most cited of 420 All 420 (JSON)