# AIM 2020 Challenge on Image Extreme Inpainting

arXiv: https://arxiv.org/abs/2010.01110 · HTML: https://mlsanity.com/paper/2010.01110/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2020.

## Built on by (most cited first, 7 of 7)

- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertion](https://mlsanity.com/paper/2403.18818/) (2024)
- [Magic Insert: Style-Aware Drag-and-Drop](https://mlsanity.com/paper/2407.02489/) (2024)
- [Learning Prior Feature and Attention Enhanced Image Inpainting](https://mlsanity.com/paper/2208.01837/) (2022)
- [PATMAT: Person Aware Tuning of Mask-Aware Transformer for Face Inpainting](https://mlsanity.com/paper/2304.06107/) (2023)
- [StyleGAN-induced data-driven regularization for inverse problems](https://mlsanity.com/paper/2110.03814/) (2021)
- [Towards Seamless Borders: A Method for Mitigating Inconsistencies in Image Inpainting and Outpainting](https://mlsanity.com/paper/2506.12530/) (2025)

## Abstract (arXiv)

This paper reviews the AIM 2020 challenge on extreme image inpainting. This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting. The goal of track 1 is to inpaint considerably large part of the image using no supervision but the context. Similarly, the goal of track 2 is to inpaint the image by having access to the entire semantic segmentation map of the image to inpaint. The challenge had 88 and 74 participants, respectively. 11 and 6 teams competed in the final phase of the challenge, respectively. This report gauges current solutions and set a benchmark for future extreme image inpainting methods.