AIM 2020 Challenge on Image Extreme Inpainting

Evangelos Ntavelis, Andrés Romero, Siavash Bigdeli, Radu Timofte, Zheng Hui, Xiumei Wang, Xinbo Gao, Chajin Shin, Taeoh Kim, Hanbin Son, Sangyoun Lee, Chao Li, Fu Li, Dongliang He, Shilei Wen, Errui Ding, Mengmeng Bai, Shuchen Li, Yu Zeng, Zhe Lin et al. (27 additional authors not shown)

Abstract: 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 large part of the image with no supervision. Similarly, the goal of track 2 is to inpaint the image by having access to the entire semantic segmentation map of the input. 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.