# Contextual-based Image Inpainting: Infer, Match, and Translate

arXiv: https://arxiv.org/abs/1711.08590 · HTML: https://mlsanity.com/paper/1711.08590/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2017.

## Built on by (most cited first, 30 of 50)

- [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021)
- [Image-to-Image Translation: Methods and Applications](https://mlsanity.com/paper/2101.08629/) (2021)
- [Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAE](https://mlsanity.com/paper/2103.10022/) (2021)
- [Aggregated Contextual Transformations for High-Resolution Image Inpainting](https://mlsanity.com/paper/2104.01431/) (2021)
- [PD-GAN: Probabilistic Diverse GAN for Image Inpainting](https://mlsanity.com/paper/2105.02201/) (2021)
- [Image Inpainting via Conditional Texture and Structure Dual Generation](https://mlsanity.com/paper/2108.09760/) (2021)
- [Uni-paint: A Unified Framework for Multimodal Image Inpainting with Pretrained Diffusion Model](https://mlsanity.com/paper/2310.07222/) (2023)
- [WaveFill: A Wavelet-based Generation Network for Image Inpainting](https://mlsanity.com/paper/2107.11027/) (2021)
- [Deep Learning-based Image and Video Inpainting: A Survey](https://mlsanity.com/paper/2401.03395/) (2024)
- [Bridging Global Context Interactions for High-Fidelity Image Completion](https://mlsanity.com/paper/2104.00845/) (2021)
- [Image Inpainting by End-to-End Cascaded Refinement with Mask Awareness](https://mlsanity.com/paper/2104.13743/) (2021)
- [SketchEdit: Mask-Free Local Image Manipulation with Partial Sketches](https://mlsanity.com/paper/2111.15078/) (2021)
- [Towards Efficient and Scale-Robust Ultra-High-Definition Image Demoireing](https://mlsanity.com/paper/2207.09935/) (2022)
- [AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation](https://mlsanity.com/paper/2102.12593/) (2021)
- [RORem: Training a Robust Object Remover with Human-in-the-Loop](https://mlsanity.com/paper/2501.00740/) (2025)
- [The Surprisingly Straightforward Scene Text Removal Method With Gated Attention and Region of Interest Generation: A Comprehensive Prominent Model Analysis](https://mlsanity.com/paper/2210.07489/) (2022)
- [Latent-OFER: Detect, Mask, and Reconstruct with Latent Vectors for Occluded Facial Expression Recognition](https://mlsanity.com/paper/2307.11404/) (2023)
- [Dual-cycle Constrained Bijective VAE-GAN For Tagged-to-Cine Magnetic Resonance Image Synthesis](https://mlsanity.com/paper/2101.05439/) (2021)
- [RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection](https://mlsanity.com/paper/2302.12464/) (2023)
- [Improving Text-guided Object Inpainting with Semantic Pre-inpainting](https://mlsanity.com/paper/2409.08260/) (2024)
- [FT-TDR: Frequency-guided Transformer and Top-Down Refinement Network for Blind Face Inpainting](https://mlsanity.com/paper/2108.04424/) (2021)
- [Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology](https://mlsanity.com/paper/2101.06775/) (2021)
- [Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction](https://mlsanity.com/paper/2312.16611/) (2023)
- [Shape-guided Object Inpainting](https://mlsanity.com/paper/2204.07845/) (2022)
- [Generative Memory-Guided Semantic Reasoning Model for Image Inpainting](https://mlsanity.com/paper/2110.00261/) (2021)
- [Don't Look into the Dark: Latent Codes for Pluralistic Image Inpainting](https://mlsanity.com/paper/2403.18186/) (2024)
- [High-Quality Pluralistic Image Completion via Code Shared VQGAN](https://mlsanity.com/paper/2204.01931/) (2022)
- [Reconstructing occluded Elevation Information in Terrain Maps with Self-supervised Learning](https://mlsanity.com/paper/2109.07150/) (2021)
- [Fully Context-Aware Image Inpainting with a Learned Semantic Pyramid](https://mlsanity.com/paper/2112.04107/) (2021)
- [Point Cloud Generation with Continuous Conditioning](https://mlsanity.com/paper/2202.08526/) (2022)

## Abstract (arXiv)

We study the task of image inpainting, which is to fill in the missing region of an incomplete image with plausible contents. To this end, we propose a learning-based approach to generate visually coherent completion given a high-resolution image with missing components. In order to overcome the difficulty to directly learn the distribution of high-dimensional image data, we divide the task into inference and translation as two separate steps and model each step with a deep neural network. We also use simple heuristics to guide the propagation of local textures from the boundary to the hole. We show that, by using such techniques, inpainting reduces to the problem of learning two image-feature translation functions in much smaller space and hence easier to train. We evaluate our method on several public datasets and show that we generate results of better visual quality than previous state-of-the-art methods.