Chenghua Li, R. Suganya, A. N. Rajagopalan, Nisarg Shah, Hengxing Cai, Yaning Li, Sabine Süsstrunk, Kuldeep Purohit, Radu Timofte, Kele Xu, Chu-Tak Li, P. S. Hrishikesh, C. V. Jiji, Maitreya Suin, Cong Leng, Yu Zhu, Tongtong Zhao, Zhi-Song Liu, Li-Wen Wang, Yuzhong Liu, Michael S. Brown, Densen Puthussery, Liping Dong, Zhongyun Hu, Akashdeep Jassal, Shanshan Zhao, Xin Huang, Melvin Kuriakose, Mahmoud Afifi, Sourya Dipta Das, Sabari Nathan, Qing Wang, Ruofan Zhou, Majed El Helou, M. Parisa Beham, Jian Cheng, and Zhuolong Jiang
We review the AIM 2020 challenge on virtual image relighting and illumination estimation. This paper presents the novel VIDIT dataset used in the challenge and the different proposed solutions and final evaluation results over the 3 challenge tracks. The first track considered one-to-one relighting; the objective was to relight an input photo of a scene with a different color temperature and illuminant orientation (i.e., light source position). The goal of the second track was to estimate illumination settings, namely the color temperature and orientation, from a given image. Lastly, the third track dealt with any-to-any relighting, thus a generalization of the first track. The target color temperature and orientation, rather than being pre-determined, are instead given by a guide image. Participants were allowed to make use of their track 1 and 2 solutions for track 3. The tracks had 94, 52, and 56 registered participants, respectively, leading to 20 confirmed submissions in the final competition stage., ECCVW 2020. Data and more information on https://github.com/majedelhelou/VIDIT