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
Published: 2025-04-15 16:02:47

Abstract: 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/.