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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2604.10655 |
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| _version_ | 1866910127354806272 |
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| author | Qian, Chenghao Li, Xin Jin, Yeying Sun, Shangguan Zhong, Yilian Chen, Yuxiang Yin, Shibo Fang, Yushun Zhu, Xilei Wang, Yahui Lu, Chen Fu, Ying Tian, Jianan Zhang, Jifan Zhou, Chen Jiang, Junyang Sun, Yuping Shi, Zhuohang Liu, Xiaojing Liu, Jiao Zhou, Yatong Liu, Shuai Deng, Qiang Mi, Jiajia Luo, Qianhao Li, Weiling |
| author_facet | Qian, Chenghao Li, Xin Jin, Yeying Sun, Shangguan Zhong, Yilian Chen, Yuxiang Yin, Shibo Fang, Yushun Zhu, Xilei Wang, Yahui Lu, Chen Fu, Ying Tian, Jianan Zhang, Jifan Zhou, Chen Jiang, Junyang Sun, Yuping Shi, Zhuohang Liu, Xiaojing Liu, Jiao Zhou, Yatong Liu, Shuai Deng, Qiang Mi, Jiajia Luo, Qianhao Li, Weiling |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_10655 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LoViF 2026 The First Challenge on Weather Removal in Videos Qian, Chenghao Li, Xin Jin, Yeying Sun, Shangguan Zhong, Yilian Chen, Yuxiang Yin, Shibo Fang, Yushun Zhu, Xilei Wang, Yahui Lu, Chen Fu, Ying Tian, Jianan Zhang, Jifan Zhou, Chen Jiang, Junyang Sun, Yuping Shi, Zhuohang Liu, Xiaojing Liu, Jiao Zhou, Yatong Liu, Shuai Deng, Qiang Mi, Jiajia Luo, Qianhao Li, Weiling Computer Vision and Pattern Recognition Artificial Intelligence Multimedia 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/. |
| title | LoViF 2026 The First Challenge on Weather Removal in Videos |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Multimedia |
| url | https://arxiv.org/abs/2604.10655 |