Scaling Video Matting via a Learned Quality Evaluator

Peiqing Yang¹  Shangchen Zhou^1†  Kai Hao¹  Qingyi Tao² 

¹S-Lab, Nanyang Technological University  ²SenseTime Research, Singapore 
^†Project lead

MatAnyone 2 is a practical human video matting framework that preserves fine details by avoiding segmentation-like boundaries, while also shows enhanced robustness under challenging real-world conditions.

🎥 For more visual results, go checkout our project page

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📮 Update

• [2025.12] This repo is created.

🔎 Overview

🛠️ Data Pipeline

📑 Citation

If you find our repo useful for your research, please consider citing our paper:

@InProceedings{yang2025matanyone2,
   title     = {{MatAnyone 2}: Scaling Video Matting via a Learned Quality Evaluator},
   author    = {Yang, Peiqing and Zhou, Shangchen and Hao, Kai and Tao, Qingyi},
   booktitle = {arXiv preprint arXiv:2512.11782},
   year      = {2025}
}

📧 Contact

If you have any questions, please feel free to reach us at peiqingyang99@outlook.com.