LoViF 2026 The First Challenge on Weather Removal in Videos

Chenghao Qian, Xin Li, Yeying Jin, Shangquan Sun, Yilian Zhong, Yuxiang Chen, Shibo Yin, Yushun Fang, Xilei Zhu, Yahui Wang, Chen Lu, Ying Fu, Jianan Tian, Jifan Zhang, Chen Zhou, Junyang Jiang, Yuping Sun, Zhuohang Shi, Xiaojing Liu, Jiao Liu, Yatong Zhou, Shuai Liu, Qiang Deng, Jiajia Mi, Qianhao Luo, Weiling Li; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2026, pp. 5047-5056

This paper presents a review of the LoViF 2026 Challenge on Weather Removal in Videos. The challenge encourages the development of methods for restoring clean videos from inputs degraded by adverse weather conditions such as rain and snow, with an emphasis on achieving visually plausible and temporally consistent results while preserving scene structure and motion dynamics. To support this task, we introduce a new short-form WRV dataset tailored for video weather removal. It consists of 18 videos (1,216 synthesized frames paired with 1,216 real-world ground-truth frames) at a resolution of 832 x 480, and is split into training, validation, and test sets with a ratio of 1:1:1. The goal of this challenge is to advance robust and realistic video restoration under real-world weather conditions, with evaluation protocols that jointly consider fidelity and perceptual quality. The challenge attracted 37 participants and received 5 valid final submissions with corresponding fact sheets, contributing to progress in weather removal for videos. The project is publicly available at https://www.codabench.org/competitions/13462/.