PVUW 2025 Challenge Report: Advances in Pixel-level Understanding of
  Complex Videos in the Wild
Authors: name: Henghui Ding, name: Chang Liu, name: Nikhila Ravi, name: Shuting He, name: Yunchao Wei, name: Song Bai, name: Philip Torr, name: Kehuan Song, name: Xinglin Xie, name: Kexin Zhang, name: Licheng Jiao, name: Lingling Li, name: Shuyuan Yang, name: Xuqiang Cao, name: Linnan Zhao, name: Jiaxuan Zhao, name: Fang Liu, name: Mengjiao Wang, name: Junpei Zhang, name: Xu Liu, name: Yuting Yang, name: Mengru Ma, name: Hao Fang, name: Runmin Cong, name: Xiankai Lu, name: Zhiyang Che, name: Wei Zhan, name: Tianming Liang, name: Haichao Jiang, name: Wei-Shi Zheng, name: Jian-Fang Hu, name: Haobo Yuan, name: Xiangtai Li, name: Tao Zhang, name: Lu Qi, name: Ming-Hsuan Yang

This report provides a comprehensive overview of the 4th Pixel-level Video
Understanding in the Wild (PVUW) Challenge, held in conjunction with CVPR 2025.
It summarizes the challenge outcomes, participating methodologies, and future
research directions. The challenge features two tracks: MOSE, which focuses on
complex scene video object segmentation, and MeViS, which targets
motion-guided, language-based video segmentation. Both tracks introduce new,
more challenging datasets designed to better reflect real-world scenarios.
Through detailed evaluation and analysis, the challenge offers valuable
insights into the current state-of-the-art and emerging trends in complex video
segmentation. More information can be found on the workshop website:
https://pvuw.github.io/.