pq-yang
175 lines
6.9 KiB
Markdown
175 lines
6.9 KiB
Markdown
<div align="center">
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<div style="text-align: center;">
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<img src="./assets/matanyone2_logo.png" alt="MatAnyone Logo" style="height: 52px;">
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<h2>Scaling Video Matting via a Learned Quality Evaluator</h2>
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</div>
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<div>
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<a href='https://pq-yang.github.io/' target='_blank'>Peiqing Yang</a><sup>1</sup> 
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<a href='https://shangchenzhou.com/' target='_blank'>Shangchen Zhou</a><sup>1†</sup> 
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<a href="https://www.linkedin.com/in/kai-hao-794321382/" target='_blank'>Kai Hao</a><sup>1</sup> 
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<a href="https://scholar.google.com.sg/citations?user=fMXnSGMAAAAJ&hl=en/" target='_blank'>Qingyi Tao</a><sup>2</sup> 
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</div>
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<div>
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<sup>1</sup>S-Lab, Nanyang Technological University 
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<sup>2</sup>SenseTime Research, Singapore 
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<br>
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<sup>†</sup>Project lead
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</div>
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<div>
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<h4 align="center">
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<a href="https://pq-yang.github.io/projects/MatAnyone2/" target='_blank'>
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<img src="https://img.shields.io/badge/😈-Project%20Page-blue">
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</a>
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<a href="https://arxiv.org/abs/2512.11782" target='_blank'>
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<img src="https://img.shields.io/badge/arXiv-2501.14677-b31b1b.svg">
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</a>
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<a href="https://www.youtube.com/watch?v=tyi8CNyjOhc&lc=Ugw1OS7z5QbW29RZCFZ4AaABAg" target='_blank'>
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<img src="https://img.shields.io/badge/Demo%20Video-%23FF0000.svg?logo=YouTube&logoColor=white">
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</a>
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</a>
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<img src="https://api.infinitescript.com/badgen/count?name=sczhou/MatAnyone2<ext=Visitors&color=3977dd">
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</h4>
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</div>
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<strong>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.</strong>
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<div style="width: 100%; text-align: center; margin:auto;">
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<img style="width:100%" src="assets/teaser.jpg">
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</div>
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:movie_camera: For more visual results, go checkout our <a href="https://pq-yang.github.io/projects/MatAnyone2/" target="_blank">project page</a>
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---
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</div>
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## 📮 Update
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- [2026.03] Release inference codes and gradio demo.
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- [2025.12] This repo is created.
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## 🏄🏻♀️ TODO
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- [x] Release inference codes and gradio demo.
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- [ ] Release evaluation codes.
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- [ ] Release training codes for video matting model.
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- [ ] Release checkpoint and training codes for quality evaluator model.
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- [ ] Release real-world video matting dataset **VMReal**.
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## 🔎 Overview
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## 🔧 Installation
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1. Clone Repo
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```bash
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git clone https://github.com/pq-yang/MatAnyone2
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cd MatAnyone2
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```
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2. Create Conda Environment and Install Dependencies
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```bash
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# create new conda env
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conda create -n matanyone2 python=3.10 -y
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conda activate matanyone2
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# install python dependencies
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pip install -e .
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# [optional] install python dependencies for gradio demo
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pip3 install -r hugging_face/requirements.txt
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```
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## 🔥 Inference
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### Download Model
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Download our pretrained model from [MatAnyone 2](https://github.com/pq-yang/MatAnyone2/releases/download/v1.0.0/matanyone2.pth) to the `pretrained_models` folder (pretrained model can also be automatically downloaded during the first inference).
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The directory structure will be arranged as:
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```
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pretrained_models
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|- matanyone2.pth
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```
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### Quick Test
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We provide some examples in the [`inputs`](./inputs) folder. **For each run, we take a video and its first-frame segmenatation mask as input.** <u>The segmenation mask could be obtained from interactive segmentation models such as [SAM2 demo](https://huggingface.co/spaces/fffiloni/SAM2-Image-Predictor)</u>. For example, the directory structure can be arranged as:
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```
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inputs
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|- video
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|- test-sample1 # folder containing all frames
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|- test-sample2.mp4 # .mp4, .mov, .avi
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|- mask
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|- test-sample1.png # mask for targer person(s)
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|- test-sample2.png
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```
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Run the following command to try it out:
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```shell
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# intput format: video folder
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python inference_matanyone2.py -i inputs/video/test-sample1 -m inputs/mask/test-sample1.png
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# intput format: mp4
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python inference_matanyone2.py -i inputs/video/test-sample2.mp4 -m inputs/mask/test-sample2.png
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```
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The results will be saved in the `results` folder, including the foreground output video and the alpha output video.
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- If you want to save the results as per-frame images, you can set `--save_image`.
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- If you want to set a limit for the maximum input resolution, you can set `--max_size`, and the video will be downsampled if min(w, h) exceeds. By default, we don't set the limit.
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## 🎪 Interactive Demo
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To get rid of the preparation for first-frame segmentation mask, we prepare a gradio demo on [hugging face](https://huggingface.co/spaces/PeiqingYang/MatAnyone2) and could also **launch locally**. Just drop your video/image, assign the target masks with a few clicks, and get the the matting results!
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*We integrate MatAnyone Series in the demo. [MatAnyone 2](https://github.com/pq-yang/MatAnyone2) is the default model. You can also choose [MatAnyone](https://github.com/pq-yang/MatAnyone) as your processing model in "Model Selection".*
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```shell
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cd hugging_face
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# install python dependencies
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pip3 install -r requirements.txt # FFmpeg required
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# launch the demo
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python app.py
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```
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By launching, an interactive interface will appear as follow.
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## 🛠️ Data Pipeline
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## 📑 Citation
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If you find our repo useful for your research, please consider citing our paper:
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```bibtex
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@InProceedings{yang2026matanyone2,
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title = {{MatAnyone 2}: Scaling Video Matting via a Learned Quality Evaluator},
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author = {Yang, Peiqing and Zhou, Shangchen and Hao, Kai and Tao, Qingyi},
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booktitle = {CVPR},
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year = {2026}
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}
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@inProceedings{yang2025matanyone,
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title = {{MatAnyone}: Stable Video Matting with Consistent Memory Propagation},
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author = {Yang, Peiqing and Zhou, Shangchen and Zhao, Jixin and Tao, Qingyi and Loy, Chen Change},
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booktitle = {CVPR},
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year = {2025}
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}
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```
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## 📝 License
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This project is licensed under <a rel="license" href="./LICENSE">NTU S-Lab License 1.0</a>. Redistribution and use should follow this license.
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## 👏 Acknowledgement
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This project is built upon [MatAnyone](https://github.com/pq-yang/MatAnyone) and [Cutie](https://github.com/hkchengrex/Cutie), with matting dataset files adapted from [RVM](https://github.com/PeterL1n/RobustVideoMatting). The interactive demo is adapted from [ProPainter](https://github.com/sczhou/ProPainter), leveraging segmentation capabilities from [Segment Anything Model](https://github.com/facebookresearch/segment-anything) and [Segment Anything Model 2](https://github.com/facebookresearch/sam2). Thanks for their awesome works!
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## 📧 Contact
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If you have any questions, please feel free to reach us at `peiqingyang99@outlook.com`.
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