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.

:movie_camera: For more visual results, go checkout our project page ---

## 📮 Update - [2025.12] This repo is created. ## 🔎 Overview ![overall_structure](assets/matanyone1vs2.jpg) ## 🛠️ Data Pipeline ![data_pipeline](assets/data_pipeline.jpg) ## 📑 Citation If you find our repo useful for your research, please consider citing our paper: ```bibtex @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`.