# Image Inpainting via Generative Multi-column Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1810.08771 · HTML: https://mlsanity.com/paper/1810.08771/

## Key facts

- Cited by 81 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2018.

## Built on by (most cited first, 30 of 81)

- [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021)
- [Large Scale Image Completion via Co-Modulated Generative Adversarial Networks](https://mlsanity.com/paper/2103.10428/) (2021)
- [MAT: Mask-Aware Transformer for Large Hole Image Inpainting](https://mlsanity.com/paper/2203.15270/) (2022)
- [ECON: Explicit Clothed humans Optimized via Normal integration](https://mlsanity.com/paper/2212.07422/) (2022)
- [SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting with Neural Radiance Fields](https://mlsanity.com/paper/2211.12254/) (2022)
- [Aggregated Contextual Transformations for High-Resolution Image Inpainting](https://mlsanity.com/paper/2104.01431/) (2021)
- [Capturing and Animation of Body and Clothing from Monocular Video](https://mlsanity.com/paper/2210.01868/) (2022)
- [PD-GAN: Probabilistic Diverse GAN for Image Inpainting](https://mlsanity.com/paper/2105.02201/) (2021)
- [MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution](https://mlsanity.com/paper/2106.02299/) (2021)
- [Open-World Entity Segmentation](https://mlsanity.com/paper/2107.14228/) (2021)
- [Image Synthesis via Semantic Composition](https://mlsanity.com/paper/2109.07053/) (2021)
- [WaveFill: A Wavelet-based Generation Network for Image Inpainting](https://mlsanity.com/paper/2107.11027/) (2021)
- [Learning Disentangled Avatars with Hybrid 3D Representations](https://mlsanity.com/paper/2309.06441/) (2023)
- [Multi-View Consistent Generative Adversarial Networks for 3D-aware Image Synthesis](https://mlsanity.com/paper/2204.06307/) (2022)
- [Reference-guided Controllable Inpainting of Neural Radiance Fields](https://mlsanity.com/paper/2304.09677/) (2023)
- [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)
- [Drivable Avatar Clothing: Faithful Full-Body Telepresence with Dynamic Clothing Driven by Sparse RGB-D Input](https://mlsanity.com/paper/2310.05917/) (2023)
- [Image Inpainting with External-internal Learning and Monochromic Bottleneck](https://mlsanity.com/paper/2104.09068/) (2021)
- [Paste, Inpaint and Harmonize via Denoising: Subject-Driven Image Editing with Pre-Trained Diffusion Model](https://mlsanity.com/paper/2306.07596/) (2023)
- [Bi-level Feature Alignment for Versatile Image Translation and Manipulation](https://mlsanity.com/paper/2107.03021/) (2021)
- [MLReal: Bridging the gap between training on synthetic data and real data applications in machine learning](https://mlsanity.com/paper/2109.05294/) (2021)
- [PalGAN: Image Colorization with Palette Generative Adversarial Networks](https://mlsanity.com/paper/2210.11204/) (2022)
- [Learning Image-Adaptive Codebooks for Class-Agnostic Image Restoration](https://mlsanity.com/paper/2306.06513/) (2023)
- [Bridging the Visual Gap: Wide-Range Image Blending](https://mlsanity.com/paper/2103.15149/) (2021)
- [GRIDS: Grouped Multiple-Degradation Restoration with Image Degradation Similarity](https://mlsanity.com/paper/2407.12273/) (2024)
- [Generalised Image Outpainting with U-Transformer](https://mlsanity.com/paper/2201.11403/) (2022)
- [Unbiased Multi-Modality Guidance for Image Inpainting](https://mlsanity.com/paper/2208.11844/) (2022)
- [Markpainting: Adversarial Machine Learning meets Inpainting](https://mlsanity.com/paper/2106.00660/) (2021)
- [Image Inpainting via Iteratively Decoupled Probabilistic Modeling](https://mlsanity.com/paper/2212.02963/) (2022)

## Abstract (arXiv)

In this paper, we propose a generative multi-column network for image inpainting. This network synthesizes different image components in a parallel manner within one stage. To better characterize global structures, we design a confidence-driven reconstruction loss while an implicit diversified MRF regularization is adopted to enhance local details. The multi-column network combined with the reconstruction and MRF loss propagates local and global information derived from context to the target inpainting regions. Extensive experiments on challenging street view, face, natural objects and scenes manifest that our method produces visual compelling results even without previously common post-processing.