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/.

翻译：本文对LoViF 2026视频天气去除挑战赛进行了综述。该挑战赛旨在激励研究者开发从雨雪等恶劣天气条件退化的输入视频中恢复清晰视频的方法，重点是在保持场景结构与运动动态的同时，实现视觉上可信、时间上一致的结果。为支撑该任务，我们引入了一个专为视频天气去除定制的新型短时WRV数据集。该数据集包含18个视频，共计1216帧合成帧及其对应的1216帧真实世界真值帧，分辨率为832×480，并以1:1:1的比例划分为训练集、验证集和测试集。本挑战赛的目标是在真实世界天气条件下推进稳健且逼真的视频复原，其评估协议同时考虑了保真度与感知质量。该挑战赛吸引了37名参与者，并收到了5份包含相应技术说明书的有效最终提交方案，为视频天气去除领域的发展做出了贡献。该项目公开访问网址为：https://www.codabench.org/competitions/13462/。