PVUW 2025 Challenge Report: Advances in Pixel-level Understanding of Complex Videos in the Wild

Henghui Ding, Chang Liu, Nikhila Ravi, Shuting He, Yunchao Wei, Song Bai, Philip Torr; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2025, pp. 2669-2678

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/.

Related Material

[pdf] [arXiv]
[bibtex]
@InProceedings{Ding_2025_CVPR, author = {Ding, Henghui and Liu, Chang and Ravi, Nikhila and He, Shuting and Wei, Yunchao and Bai, Song and Torr, Philip}, title = {PVUW 2025 Challenge Report: Advances in Pixel-level Understanding of Complex Videos in the Wild}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2025}, pages = {2669-2678} }