title: PVUW 2025 Challenge Report: Advances in Pixel-level Understanding of
    Complex Videos in the Wild
summary: 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/.
paper_authors._id: 67ff25b765b52d1b69c1f6c1
paper_authors.name: Henghui Ding
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-04-16T03:55:17.825Z
paper_authors._id: 67ff29ecbf6889a333c69c7a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/fYIJFtF0uciB-1wb0lmTY.png
paper_authors.fullname: Henghui Ding
paper_authors.type: user
paper_authors.user: HenghuiDing
paper_authors._id: 67ff25b765b52d1b69c1f6c2
paper_authors.name: Chang Liu
paper_authors._id: 67ff25b765b52d1b69c1f6c3
paper_authors.name: Nikhila Ravi
paper_authors._id: 67ff25b765b52d1b69c1f6c4
paper_authors.name: Shuting He
paper_authors._id: 67ff25b765b52d1b69c1f6c5
paper_authors.name: Yunchao Wei
paper_authors._id: 67ff25b765b52d1b69c1f6c6
paper_authors.name: Song Bai
paper_authors._id: 67ff25b765b52d1b69c1f6c7
paper_authors.name: Philip Torr
paper_authors._id: 67ff25b765b52d1b69c1f6c8
paper_authors.name: Kehuan Song
paper_authors._id: 67ff25b765b52d1b69c1f6c9
paper_authors.name: Xinglin Xie
paper_authors._id: 67ff25b765b52d1b69c1f6ca
paper_authors.name: Kexin Zhang
paper_authors._id: 67ff25b765b52d1b69c1f6cb
paper_authors.name: Licheng Jiao
paper_authors._id: 67ff25b765b52d1b69c1f6cc
paper_authors.name: Lingling Li
paper_authors._id: 67ff25b765b52d1b69c1f6cd
paper_authors.name: Shuyuan Yang
paper_authors._id: 67ff25b765b52d1b69c1f6ce
paper_authors.name: Xuqiang Cao
paper_authors._id: 67ff25b765b52d1b69c1f6cf
paper_authors.name: Linnan Zhao
paper_authors._id: 67ff25b765b52d1b69c1f6d0
paper_authors.name: Jiaxuan Zhao
paper_authors._id: 67ff25b765b52d1b69c1f6d1
paper_authors.name: Fang Liu
paper_authors._id: 67ff25b765b52d1b69c1f6d2
paper_authors.name: Mengjiao Wang
paper_authors._id: 67ff25b765b52d1b69c1f6d3
paper_authors.name: Junpei Zhang
paper_authors._id: 67ff25b765b52d1b69c1f6d4
paper_authors.name: Xu Liu
paper_authors._id: 67ff25b765b52d1b69c1f6d5
paper_authors.name: Yuting Yang
paper_authors._id: 67ff25b765b52d1b69c1f6d6
paper_authors.name: Mengru Ma
paper_authors._id: 67ff25b765b52d1b69c1f6d7
paper_authors.name: Hao Fang
paper_authors._id: 67ff25b765b52d1b69c1f6d8
paper_authors.name: Runmin Cong
paper_authors._id: 67ff25b765b52d1b69c1f6d9
paper_authors.name: Xiankai Lu
paper_authors._id: 67ff25b765b52d1b69c1f6da
paper_authors.name: Zhiyang Che
paper_authors._id: 67ff25b765b52d1b69c1f6db
paper_authors.name: Wei Zhan
paper_authors._id: 67ff25b765b52d1b69c1f6dc
paper_authors.name: Tianming Liang
paper_authors._id: 67ff25b765b52d1b69c1f6dd
paper_authors.name: Haichao Jiang
paper_authors._id: 67ff25b765b52d1b69c1f6de
paper_authors.name: Wei-Shi Zheng
paper_authors._id: 67ff25b765b52d1b69c1f6df
paper_authors.name: Jian-Fang Hu
paper_authors._id: 67ff25b765b52d1b69c1f6e0
paper_authors.name: Haobo Yuan
paper_authors._id: 67ff25b765b52d1b69c1f6e1
paper_authors.name: Xiangtai Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-16T08:42:58.429Z
paper_authors._id: 63958b4414513eaf9029ebf1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/U1g5H071pWRswGAG9UTpo.png
paper_authors.fullname: Xiangtai Li
paper_authors.type: user
paper_authors.user: LXT
paper_authors._id: 67ff25b765b52d1b69c1f6e2
paper_authors.name: Tao Zhang
paper_authors._id: 67ff25b765b52d1b69c1f6e3
paper_authors.name: Lu Qi
paper_authors._id: 67ff25b765b52d1b69c1f6e4
paper_authors.name: Ming-Hsuan Yang
paper_title: PVUW 2025 Challenge Report: Advances in Pixel-level Understanding of
    Complex Videos in the Wild
paper_summary: 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/.
paper_ai_summary: The PVUW Challenge provides insights into complex video segmentation by evaluating methodologies on challenging datasets in two tracks: MOSE and MeViS.
paper_ai_keywords: complex scene video object segmentation motion-guided language-based video segmentation