# Region-wise Generative Adversarial ImageInpainting for Large Missing Areas

arXiv: https://arxiv.org/abs/1909.12507 · HTML: https://mlsanity.com/paper/1909.12507/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2019.

## Built on by (most cited first, 18 of 18)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [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)
- [Designing a Better Asymmetric VQGAN for StableDiffusion](https://mlsanity.com/paper/2306.04632/) (2023)
- [Exploring the Relationship between Architecture and Adversarially Robust Generalization](https://mlsanity.com/paper/2209.14105/) (2022)
- [RORem: Training a Robust Object Remover with Human-in-the-Loop](https://mlsanity.com/paper/2501.00740/) (2025)
- [Look Ma, No Hands! Agent-Environment Factorization of Egocentric Videos](https://mlsanity.com/paper/2305.16301/) (2023)
- [Gradpaint: Gradient-Guided Inpainting with Diffusion Models](https://mlsanity.com/paper/2309.09614/) (2023)
- [PATMAT: Person Aware Tuning of Mask-Aware Transformer for Face Inpainting](https://mlsanity.com/paper/2304.06107/) (2023)
- [SFI-Swin: Symmetric Face Inpainting with Swin Transformer by Distinctly Learning Face Components Distributions](https://mlsanity.com/paper/2301.03130/) (2023)
- [WavePaint: Resource-efficient Token-mixer for Self-supervised Inpainting](https://mlsanity.com/paper/2307.00407/) (2023)
- [Deep Spectral Prior](https://mlsanity.com/paper/2505.19873/) (2025)
- [Towards Unified Keyframe Propagation Models](https://mlsanity.com/paper/2205.09731/) (2022)
- [CWP: Instance complexity weighted channel-wise soft masks for network pruning](https://mlsanity.com/paper/2209.03534/) (2022)
- [Windowed-FourierMixer: Enhancing Clutter-Free Room Modeling with Fourier Transform](https://mlsanity.com/paper/2402.18287/) (2024)
- [UrbanDIFF: A Denoising Diffusion Model for Spatial Gap Filling of Urban Land Surface Temperature Under Dense Cloud Cover](https://mlsanity.com/paper/2512.17782/) (2025)
- [AdaEraser: Training-Free Object Removal via Adaptive Attention Suppression](https://mlsanity.com/paper/2605.15921/) (2026)

## Abstract (arXiv)

Recently deep neutral networks have achieved promising performance for filling large missing regions in image inpainting tasks. They usually adopted the standard convolutional architecture over the corrupted image, leading to meaningless contents, such as color discrepancy, blur and artifacts. Moreover, most inpainting approaches cannot well handle the large continuous missing area cases. To address these problems, we propose a generic inpainting framework capable of handling with incomplete images on both continuous and discontinuous large missing areas, in an adversarial manner. From which, region-wise convolution is deployed in both generator and discriminator to separately handle with the different regions, namely existing regions and missing ones. Moreover, a correlation loss is introduced to capture the non-local correlations between different patches, and thus guides the generator to obtain more information during inference. With the help of our proposed framework, we can restore semantically reasonable and visually realistic images. Extensive experiments on three widely-used datasets for image inpainting tasks have been conducted, and both qualitative and quantitative experimental results demonstrate that the proposed model significantly outperforms the state-of-the-art approaches, both on the large continuous and discontinuous missing areas.