mlsanityData to 7 Oct 2026

SinGAN: Learning a Generative Model from a Single Natural Image

arXiv 1905.01164 ↗First posted 2 May 2019cs.CVcited by 348

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Cited by 51 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We introduce SinGAN, an unconditional generative model that can be learned from a single natural image. Our model is trained to capture the internal distribution of patches within the image, and is then able to generate high quality, diverse samples that carry the same visual content as the image. SinGAN contains a pyramid of fully convolutional GANs, each responsible for learning the patch distribution at a different scale of the image. This allows generating new samples of arbitrary size and aspect ratio, that have significant variability, yet maintain both the global structure and the fine textures of the training image. In contrast to previous single image GAN schemes, our approach is not limited to texture images, and is not conditional (i.e. it generates samples from noise). User studies confirm that the generated samples are commonly confused to be real images. We illustrate the utility of SinGAN in a wide range of image manipulation tasks.

Key facts. Cited by 348 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 May 2019.

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Citing papers per year: 2021: 98; 2022: 77; 2023: 77; 2024: 49; 2025: 32; 2026: 15.

348 papers.

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  1. 2022Generative Adversarial Networkscited by 5,603
  2. 2021ILVR: Conditioning Method for Denoising Diffusion Probabilistic Modelscited by 634
  3. 2023T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representationscited by 577
  4. 2021Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesiscited by 464
  5. 2023DynamiCrafter: Animating Open-domain Images with Video Diffusion Priorscited by 463
  6. 2021GAN Inversion: A Surveycited by 366
  7. 2021Text2Mesh: Text-Driven Neural Stylization for Meshescited by 332
  8. 2022Text2LIVE: Text-Driven Layered Image and Video Editingcited by 312
  9. 2023Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion Modelscited by 297
  10. 2023Text-to-image Diffusion Models in Generative AI: A Surveycited by 264
  11. 2024Motion-I2V: Consistent and Controllable Image-to-Video Generation with Explicit Motion Modelingcited by 209
  12. 2022Versatile Diffusion: Text, Images and Variations All in One Diffusion Modelcited by 199
38 more
  1. 2023Generative Diffusion Prior for Unified Image Restoration and Enhancementcited by 198
  2. 2023ZipIt! Merging Models from Different Tasks without Trainingcited by 178
  3. 2022SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Imagecited by 165
  4. 2022SINE: SINgle Image Editing with Text-to-Image Diffusion Modelscited by 159
  5. 2021GNeRF: GAN-based Neural Radiance Field without Posed Cameracited by 153
  6. 2022SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting with Neural Radiance Fieldscited by 144
  7. 2021DriveGAN: Towards a Controllable High-Quality Neural Simulationcited by 125
  8. 2021Image-to-Image Translation: Methods and Applicationscited by 116
  9. 2022EpiGRAF: Rethinking training of 3D GANscited by 108
  10. 2021NeRF-SR: High-Quality Neural Radiance Fields using Supersamplingcited by 95
  11. 2022Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learningcited by 93
  12. 2021Learning to See by Looking at Noisecited by 92
  13. 2024MOFA-Video: Controllable Image Animation via Generative Motion Field Adaptions in Frozen Image-to-Video Diffusion Modelcited by 87
  14. 2022InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Imagescited by 82
  15. 2022A Survey on Long-Tailed Visual Recognitioncited by 75
  16. 2022Any-resolution Training for High-resolution Image Synthesiscited by 72
  17. 2022NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesiscited by 71
  18. 2021Aligning Latent and Image Spaces to Connect the Unconnectablecited by 70
  19. 2021The Spatially-Correlative Loss for Various Image Translation Taskscited by 66
  20. 2023AnimateAnything: Fine-Grained Open Domain Image Animation with Motion Guidancecited by 66
  21. 2023Single Motion Diffusioncited by 65
  22. 2021InfinityGAN: Towards Infinite-Pixel Image Synthesiscited by 61
  23. 2021A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detectioncited by 60
  24. 2022SinDDM: A Single Image Denoising Diffusion Modelcited by 60
  25. 2022SinFusion: Training Diffusion Models on a Single Image or Videocited by 58
  26. 2024SCube: Instant Large-Scale Scene Reconstruction using VoxSplatscited by 56
  27. 2023RoSteALS: Robust Steganography using Autoencoder Latent Spacecited by 54
  28. 2021A Theory of the Distortion-Perception Tradeoff in Wasserstein Spacecited by 52
  29. 2024Follow-Your-Click: Open-domain Regional Image Animation via Short Promptscited by 52
  30. 2021Drop the GAN: In Defense of Patches Nearest Neighbors as Single Image Generative Modelscited by 51
  31. 2022DiffDreamer: Towards Consistent Unsupervised Single-view Scene Extrapolation with Conditional Diffusion Modelscited by 51
  32. 2022CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfercited by 46
  33. 2021Training GANs with Stronger Augmentations via Contrastive Discriminatorcited by 45
  34. 2021SinGAN-Seg: Synthetic training data generation for medical image segmentationcited by 43
  35. 2022SinDiffusion: Learning a Diffusion Model from a Single Natural Imagecited by 42
  36. 2024Towards a Mechanistic Explanation of Diffusion Model Generalizationcited by 42
  37. 2021Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfercited by 41
  38. 2021One-Shot Generative Domain Adaptationcited by 41

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