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/.
@article{arxiv.2604.10655,
title = {LoViF 2026 The First Challenge on Weather Removal in Videos},
author = {Chenghao Qian and Xin Li and Yeying Jin and Shangguan Sun and Yilian Zhong and Yuxiang Chen and Shibo Yin and Yushun Fang and Xilei Zhu and Yahui Wang and Chen Lu and Ying Fu and Jianan Tian and Jifan Zhang and Chen Zhou and Junyang Jiang and Yuping Sun and Zhuohang Shi and Xiaojing Liu and Jiao Liu and Yatong Zhou and Shuai Liu and Qiang Deng and Jiajia Mi and Qianhao Luo and Weiling Li},
journal= {arXiv preprint arXiv:2604.10655},
year = {2026}
}