mlsanityData to 7 Oct 2026

Internal Diverse Image Completion

arXiv 2212.10280 ↗First posted 18 Dec 2022cs.CVcited by 2

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Abstract (arXiv)

Image completion is widely used in photo restoration and editing applications, e.g. for object removal. Recently, there has been a surge of research on generating diverse completions for missing regions. However, existing methods require large training sets from a specific domain of interest, and often fail on general-content images. In this paper, we propose a diverse completion method that does not require a training set and can thus treat arbitrary images from any domain. Our internal diverse completion (IDC) approach draws inspiration from recent single-image generative models that are trained on multiple scales of a single image, adapting them to the extreme setting in which only a small portion of the image is available for training. We illustrate the strength of IDC on several datasets, using both user studies and quantitative comparisons.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 31 of its 56 references that are in the map. First posted on arXiv 18 Dec 2022.

Builds on

31 of 56 references.

References · 31

  1. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  2. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  3. 2014Deep Learning Face Attributes in the Wildcited by 3,679
  4. 2017Improved Training of Wasserstein GANscited by 2,223
  5. 2016Context Encoders: Feature Learning by Inpaintingcited by 1,424
  6. 2015LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loopcited by 1,350
  7. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
  8. 2017Deep Image Priorcited by 1,097
  9. 2021Resolution-robust Large Mask Inpainting with Fourier Convolutionscited by 730
  10. 2019MaskGAN: Towards Diverse and Interactive Facial Image Manipulationcited by 624
  11. 2018Free-Form Image Inpainting with Gated Convolutioncited by 594
  12. 2018Generative Image Inpainting with Contextual Attentioncited by 578
19 more
  1. 2018Image Inpainting for Irregular Holes Using Partial Convolutionscited by 505
  2. 2017NIMA: Neural Image Assessmentcited by 387
  3. 2019SinGAN: Learning a Generative Model from a Single Natural Imagecited by 348
  4. 2021Large Scale Image Completion via Co-Modulated Generative Adversarial Networkscited by 219
  5. 2016Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networkscited by 192
  6. 2019Pluralistic Image Completioncited by 162
  7. 2019EdgeConnect: Generative Image Inpainting with Adversarial Edge Learningcited by 161
  8. 2021High-Fidelity Pluralistic Image Completion with Transformerscited by 134
  9. 2019StructureFlow: Image Inpainting via Structure-aware Appearance Flowcited by 118
  10. 2021PartImageNet: A Large, High-Quality Dataset of Partscited by 107
  11. 2021Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAEcited by 106
  12. 2020Rethinking Image Inpainting via a Mutual Encoder-Decoder with Feature Equalizationscited by 102
  13. 2019Coherent Semantic Attention for Image Inpaintingcited by 97
  14. 2016High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesiscited by 89
  15. 2021Diverse Image Inpainting with Bidirectional and Autoregressive Transformerscited by 80
  16. 2021PD-GAN: Probabilistic Diverse GAN for Image Inpaintingcited by 80
  17. 2017Generative Face Completioncited by 79
  18. 2019PiiGAN: Generative Adversarial Networks for Pluralistic Image Inpaintingcited by 17
  19. 2021Structure First Detail Next: Image Inpainting with Pyramid Generatorcited by 1

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Cited by year

Citing papers per year: 2023: 1; 2024: 1.

2 papers.

  1. 2023From Posterior Sampling to Meaningful Diversity in Image Restorationcited by 12
  2. 2024Diffusion-based image inpainting with internal learningcited by 1