---
title: Matte-Anything
app_file: matte_anything.py
sdk: gradio
sdk_version: 3.34.0
---
<div align="center">
<h1>Matte Anything!🐒</h1>
<h3> Interactive Natural Image Matting with Segment Anything Models </h3>

Authors: [Jingfeng Yao](https://github.com/JingfengYao), [Xinggang Wang](https://scholar.google.com/citations?user=qNCTLV0AAAAJ&hl=zh-CN)<sup>:email:</sup>, [Lang Ye](https://github.com/YeL6), [Wenyu Liu](http://eic.hust.edu.cn/professor/liuwenyu/)

Institute: School of EIC, HUST

(<sup>:email:</sup>) corresponding author

[![arxiv paper](https://img.shields.io/badge/arxiv-paper-orange)](https://arxiv.org/abs/2306.04121)
[![video](https://img.shields.io/badge/Demo-Video-blue)](https://github.com/hustvl/Matte-Anything/assets/74295796/dfe051c2-b5d1-442d-9eff-cd1fcfd1f51b)
[![license](https://img.shields.io/badge/license-MIT-blue)](LICENSE)
[![authors](https://img.shields.io/badge/by-hustvl-green)](https://github.com/hustvl)

</div>

![demo](https://github.com/hustvl/Matte-Anything/assets/74295796/d947f59d-b0c1-4c22-9967-d8f2bf633879)

#

## 📢 News

* **`2023/06/08`** We release arxiv tech report! 
* **`2023/06/08`** We release source codes of Matte Anything! 

The program is still in progress. You can try the early version first! Thanks for your attention. If you like Matte Anything, you may also like its previous foundation work [ViTMatte](https://github.com/hustvl/ViTMatte).

#

## 📜 Introduction

We propose Matte Anything (MatAny), an interactive natural image matting model. It could produce high-quality alpha-matte with various simple hints. The key insight of MatAny is to generate pseudo trimap automatically with contour and transparency prediction. We leverage task-specific vision models to enhance the performance of natural image matting.

![web_ui](figs/first.png)


## 🌞 Features
* Matte Anything with Simple Interaction
* High Quality Matting Results
* Ability to Process Transparent Object


## 🎮 Quick Start

Try our Matte Anything with our web-ui!

![web_ui](figs/web_ui.gif)

### Quick Installation

Install [Segment Anything Models](https://github.com/facebookresearch/segment-anything) as following:

```
pip install git+https://github.com/facebookresearch/segment-anything.git
```

Install [ViTMatte](https://github.com/hustvl/ViTMatte) as following:
```
python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
pip install -r requirements.txt
```

Install [GroundingDINO](https://github.com/IDEA-Research/GroundingDINO) as following:
```
cd Matte-Anything
git clone https://github.com/IDEA-Research/GroundingDINO.git
cd GroundingDINO
pip install -e .
```

Download pretrained models [SAM_vit_h](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth), [ViTMatte_vit_b](https://drive.google.com/file/d/1d97oKuITCeWgai2Tf3iNilt6rMSSYzkW/view?usp=sharing), and [GroundingDINO-T](https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth). Put them in ``./pretrained``

### Run our web-ui!
```
python matte_anything.py
```

### How to use
1. Upload the image and click on it (default: ``foreground point``).
2. Click ``Start!``.
3. Modify ``erode_kernel_size`` and ``dilate_kernel_size`` for a better trimap (optional).

## 🎬 Demo


https://github.com/hustvl/Matte-Anything/assets/74295796/dfe051c2-b5d1-442d-9eff-cd1fcfd1f51b


Visualization of SAM and MatAny on real-world data from [AM-2K](https://github.com/JizhiziLi/GFM) and [P3M-500](https://github.com/JizhiziLi/P3M) .
![web_ui](figs/demo1.png)
 Visualization of SAM and MatAny on [Composition-1k](https://arxiv.org/pdf/1703.03872v3.pdf)
![web_ui](figs/demo2.png)


## 📋 Todo List

- [x] adjustable trimap generation
- [x] arxiv tech report
- [ ] add example data
- [ ] support user transparency correction
- [ ] support text input
- [ ] finetune ViTMatte for better performance


## 🤝Acknowledgement

Our repo is built upon [Segment Anything](https://github.com/facebookresearch/segment-anything), [GroundingDINO](https://github.com/IDEA-Research/GroundingDINO), and [ViTMatte](https://github.com/hustvl/ViTMatte). Thanks to their work.

## Citation
```
@article{matte_anything,
  title={Matte Anything: Interactive Natural Image Matting with Segment Anything Models},
  author={Yao, Jingfeng and Wang, Xinggang and Ye, Lang and Liu, Wenyu},
  journal={arXiv preprint arXiv:2306.04121},
  year={2023}
}
```