Data to 7 Oct 2026

Semantic Image Inpainting with Deep Generative Models

arXiv 1607.07539 ↗First posted 26 Jul 2016cs.CVcited by 156

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Cited by 21 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Semantic image inpainting is a challenging task where large missing regions have to be filled based on the available visual data. Existing methods which extract information from only a single image generally produce unsatisfactory results due to the lack of high level context. In this paper, we propose a novel method for semantic image inpainting, which generates the missing content by conditioning on the available data. Given a trained generative model, we search for the closest encoding of the corrupted image in the latent image manifold using our context and prior losses. This encoding is then passed through the generative model to infer the missing content. In our method, inference is possible irrespective of how the missing content is structured, while the state-of-the-art learning based method requires specific information about the holes in the training phase. Experiments on three datasets show that our method successfully predicts information in large missing regions and achieves pixel-level photorealism, significantly outperforming the state-of-the-art methods.

Key facts. Cited by 156 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Jul 2016.

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Citing papers per year: 2021: 54; 2022: 38; 2023: 30; 2024: 17; 2025: 12; 2026: 5.

156 papers.

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2022Null-text Inversion for Editing Real Images using Guided Diffusion Modelscited by 1,168
2021SimMIM: A Simple Framework for Masked Image Modelingcited by 1,147
2022Denoising Diffusion Restoration Modelscited by 951
2021Resolution-robust Large Mask Inpainting with Fourier Convolutionscited by 730
2021ILVR: Conditioning Method for Denoising Diffusion Probabilistic Modelscited by 634
2023P+: Extended Textual Conditioning in Text-to-Image Generationcited by 242
2021HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editingcited by 208
2023Generative Diffusion Prior for Unified Image Restoration and Enhancementcited by 198
2021EditGAN: High-Precision Semantic Image Editingcited by 156
2023DDP: Diffusion Model for Dense Visual Predictioncited by 148
2021Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalizationcited by 141
2021Image-to-Image Translation: Methods and Applicationscited by 116

38 more

2023MagicPose: Realistic Human Poses and Facial Expressions Retargeting with Identity-aware Diffusioncited by 115
2021Aggregated Contextual Transformations for High-Resolution Image Inpaintingcited by 89
2021Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspectioncited by 77
2022Variational Model Inversion Attackscited by 75
2022Stitch it in Time: GAN-Based Facial Editing of Real Videoscited by 68
2023StyleDiffusion: Prompt-Embedding Inversion for Text-Based Editingcited by 68
2021Image Inpainting via Conditional Texture and Structure Dual Generationcited by 66
2022Training and Inference on Any-Order Autoregressive Models the Right Waycited by 64
2021Making Images Real Again: A Comprehensive Survey on Deep Image Compositioncited by 63
2021HDRUNet: Single Image HDR Reconstruction with Denoising and Dequantizationcited by 58
2023GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restorationcited by 58
2023RealFill: Reference-Driven Generation for Authentic Image Completioncited by 58
2021Rebooting ACGAN: Auxiliary Classifier GANs with Stable Trainingcited by 57
2022PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-step Point Moving Pathscited by 57
2021GAN Inversion for Out-of-Range Images with Geometric Transformationscited by 55
2022Third Time's the Charm? Image and Video Editing with StyleGAN3cited by 54
2022Few Shot Generative Model Adaption via Relaxed Spatial Structural Alignmentcited by 54
2021Adversarial Text-to-Image Synthesis: A Reviewcited by 52
2024Deep Learning-based Image and Video Inpainting: A Surveycited by 44
2022NeRF-In: Free-Form NeRF Inpainting with RGB-D Priorscited by 43
2023SceneGenie: Scene Graph Guided Diffusion Models for Image Synthesiscited by 41
2022Spatially-Adaptive Multilayer Selection for GAN Inversion and Editingcited by 39
2021Image Inpainting by End-to-End Cascaded Refinement with Mask Awarenesscited by 35
2022MISF: Multi-level Interactive Siamese Filtering for High-Fidelity Image Inpaintingcited by 35
2022State-of-the-Art in the Architecture, Methods and Applications of StyleGANcited by 34
2022FSGANv2: Improved Subject Agnostic Face Swapping and Reenactmentcited by 33
2021My House, My Rules: Learning Tidying Preferences with Graph Neural Networkscited by 27
2023CF-Font: Content Fusion for Few-shot Font Generationcited by 26
2021Conditional Image Generation by Conditioning Variational Auto-Encoderscited by 25
2025RORem: Training a Robust Object Remover with Human-in-the-Loopcited by 23
2021Regularising Inverse Problems with Generative Machine Learning Modelscited by 22
2023Accelerating Diffusion Models for Inverse Problems through Shortcut Samplingcited by 21
2023MapPrior: Bird's-Eye View Map Layout Estimation with Generative Modelscited by 21
2021Perceptual Indistinguishability-Net (PI-Net): Facial Image Obfuscation with Manipulable Semanticscited by 20
2022Delving StyleGAN Inversion for Image Editing: A Foundation Latent Space Viewpointcited by 18
2021JPGNet: Joint Predictive Filtering and Generative Network for Image Inpaintingcited by 17
2021Toward Spatially Unbiased Generative Modelscited by 17
2021Self-Supervised Point Cloud Completion via Inpaintingcited by 16

50 most cited of 156 All 156 (JSON)