Top AI Repos — open-source AI, indexed and scored

Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.

PeterL1n/RobustVideoMatting

Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!

43GPL-3.0Python

View on GitHub Website

Stars: 9,450
Forks: 1,199
Watchers: 134
Open issues: 114
Contributors: 5

Star history, last 90 days+6 over 5 snapshots

2026-07-24 · 9,4442026-08-06 · 9,450

Star history, last 90 days
Date	Stars
2026-07-24	9444
2026-07-25	9447
2026-07-28	9450
2026-07-30	9450
2026-08-06	9450

Today

— stars today

This week

— stars this week

This month

— stars this month

Momentum

0.0

growth rate 0.00%/day

From the README

# Robust Video Matting (RVM)

![Teaser](/documentation/image/teaser.gif)

<p align="center">English | <a href="README_zh_Hans.md">中文</a></p>

Official repository for the paper [Robust High-Resolution Video Matting with Temporal Guidance](https://peterl1n.github.io/RobustVideoMatting/). RVM is specifically designed for robust human video matting. Unlike existing neural models that process frames as independent images, RVM uses a recurrent neural network to process videos with temporal memory. RVM can perform matting in real-time on any videos without additional inputs. It achieves **4K 76FPS** and **HD 104FPS** on an Nvidia GTX 1080 Ti GPU. The project was developed at [ByteDance Inc.](https://www.bytedance.com/)

<br>

## News

* [Nov 03 2021] Fixed a bug in [train.py](https://github.com/PeterL1n/RobustVideoMatting/commit/48effc91576a9e0e7a8519f3da687c0d3522045f).
* [Sep 16 2021] Code is re-released under GPL-3.0 license.
* [Aug 25 2021] Source code and pretrained models are published.
* [Jul 27 2021] Paper is accepted by WACV 2022.

<br>

## Showreel
Watch the showreel video ([YouTube](https://youtu.be/Jvzltozpbpk), [Bilibili](https://www.bilibili.com/video/BV1Z3411B7g7/)) to see the model's performance. 

<p align="center">
    <a href="https://youtu.be/Jvzltozpbpk">
        <img src="documentation/image/showreel.gif">
    </a>
</p>

All footage in the video are available in [Google Drive](https://drive.google.com/drive/folders/1VFnWwuu-YXDKG-N6vcjK_nL7YZMFapMU?usp=sharing).

<br>


## Demo
* [Webcam Demo](https://peterl1n.github.io/RobustVideoMatting/#/demo): Run the model live in your browser. Visualize recurrent states.
* [Colab Demo](https://colab.research.google.com/drive/10z-pNKRnVNsp0Lq9tH1J_XPZ7CBC_uHm?usp=sharing): Test our model on your own videos with free GPU. 

<br>

## Download

We recommend MobileNetv3 models for most use cases. ResNet50 models are the larger variant with small performance improvements. Our model is available on various inference frameworks. See [inference documentation](documentation/inference.md) for more instructions.

<table>
    <thead>
        <tr>
            <td>Framework</td>
            <td>Download</td>
            <td>Notes</td>
        </tr>
    </thead>
    <tbody>
        <tr>
            <td>PyTorch</td>
            <td>
                <a  href="https://github.com/PeterL1n/RobustVideoMatting/releases/download/v1.0.0/rvm_mobilenetv3.pth">rvm_mobilenetv3.pth</a><br>
                <a  href="https://github.com/PeterL1n/RobustVideoMatting/releases/download/v1.0.0/rvm_resnet50.pth">rvm_resnet50.pth</a>
            </td>
            <td>
                Official weights for PyTorch. <a href="documentation/inference.md#pytorch">Doc</a>
            </td>
        </tr>
        <tr>
            <td>TorchHub</td>
            <td>
                Nothing to Download.
            </td>
            <td>
                Easiest way to use our model in your PyTorch project. <a href="documentation/inference.md#torchhub">Doc</a>
            </td>
        </tr>
        <tr>
            <td>TorchScript</td>
            <td>
                <a  href="https://github.com/PeterL1n/RobustVideoMatting/releases/download/v1.0.0/rvm_mobilenetv3_fp32.torchscript">rvm_mobilenetv3_fp32.torchscript</a><br>
                <a  href="https://github.com/PeterL1n/RobustVideoMatting/releases/download/v1.0.0/rvm_mobilenetv3_fp16.torchscript">rvm_mobilenetv3_fp16.torchscript</a><br>
                <a  href="https://github.com/PeterL1n/RobustVideoMatting/releases/download/v1.0.0/rvm_resnet50_fp32.torchscript">rvm_resnet50_fp32.torchscript</a><br>
                <a  href="https://github.com/PeterL1n/RobustVideoMatting/releases/download/v1.0.0/rvm_resnet50_fp16.torchscript">rvm_resnet50_fp16.torchscript</a>
            </td>
            <td>
                If inference on mobile, consider export int8 quantized models yourself. <a href="documentation/inference.md#torchscript">Doc</a>
            </td>
        </tr>

Excerpt of 12,293 characters

Read on GitHub

Top contributors5 shown

PeterL1n
Peter Lin · United States
22
DefTruth
DefTruth · @xlite-dev, @vipshop · China
7
AK391
Ahsen Khaliq · Huggingface
3
dcyoung
1
lanreolokoba
1

Quality score

Would you bet a product on this? Bounded 0–100 and slow moving.

43/10043

Maintenance: 5%; How recently the default branch was pushed to
Releases: 10%; Recency and cadence of tagged releases
Community: 40%; Contributor breadth and bus factor
Issue backlog: 85%; Open issues measured against the size of the audience
Documentation: 100%; README depth, plus a homepage and topics
Licensing: 55%; How freely the code can be adopted

What to know before adopting

No commits in over a year
No release in over a year
Copyleft license — derivative works must be open-sourced
Derivative works must be released under the same licence.

Details

Created: 5 years ago
Last push: 2 years ago
Last release: 5 years ago
Release tag: v1.0.0
Releases in the last year: 0
Default branch: master
Size: 9.2k KB
Owner type: User
Owner location: New York, New York, US
Discovered via: topic:computer-vision
First indexed: 14 days ago
Last synced: 9 days ago

PeterL1n/RobustVideoMatting

Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!

43GPL-3.0Python

View on GitHub Website

Stars: 9,450
Forks: 1,199
Watchers: 134
Open issues: 114
Contributors: 5

Star history, last 90 days+6 over 5 snapshots

2026-07-24 · 9,4442026-08-06 · 9,450

Star history, last 90 days
Date	Stars
2026-07-24	9444
2026-07-25	9447
2026-07-28	9450
2026-07-30	9450
2026-08-06	9450

Today

— stars today

This week

— stars this week

This month

— stars this month

Momentum

0.0

growth rate 0.00%/day

From the README

# Robust Video Matting (RVM)

![Teaser](/documentation/image/teaser.gif)

<p align="center">English | <a href="README_zh_Hans.md">中文</a></p>

Official repository for the paper [Robust High-Resolution Video Matting with Temporal Guidance](https://peterl1n.github.io/RobustVideoMatting/). RVM is specifically designed for robust human video matting. Unlike existing neural models that process frames as independent images, RVM uses a recurrent neural network to process videos with temporal memory. RVM can perform matting in real-time on any videos without additional inputs. It achieves **4K 76FPS** and **HD 104FPS** on an Nvidia GTX 1080 Ti GPU. The project was developed at [ByteDance Inc.](https://www.bytedance.com/)

<br>

## News

* [Nov 03 2021] Fixed a bug in [train.py](https://github.com/PeterL1n/RobustVideoMatting/commit/48effc91576a9e0e7a8519f3da687c0d3522045f).
* [Sep 16 2021] Code is re-released under GPL-3.0 license.
* [Aug 25 2021] Source code and pretrained models are published.
* [Jul 27 2021] Paper is accepted by WACV 2022.

<br>

## Showreel
Watch the showreel video ([YouTube](https://youtu.be/Jvzltozpbpk), [Bilibili](https://www.bilibili.com/video/BV1Z3411B7g7/)) to see the model's performance. 

<p align="center">
    <a href="https://youtu.be/Jvzltozpbpk">
        <img src="documentation/image/showreel.gif">
    </a>
</p>

All footage in the video are available in [Google Drive](https://drive.google.com/drive/folders/1VFnWwuu-YXDKG-N6vcjK_nL7YZMFapMU?usp=sharing).

<br>


## Demo
* [Webcam Demo](https://peterl1n.github.io/RobustVideoMatting/#/demo): Run the model live in your browser. Visualize recurrent states.
* [Colab Demo](https://colab.research.google.com/drive/10z-pNKRnVNsp0Lq9tH1J_XPZ7CBC_uHm?usp=sharing): Test our model on your own videos with free GPU. 

<br>

## Download

We recommend MobileNetv3 models for most use cases. ResNet50 models are the larger variant with small performance improvements. Our model is available on various inference frameworks. See [inference documentation](documentation/inference.md) for more instructions.

<table>
    <thead>
        <tr>
            <td>Framework</td>
            <td>Download</td>
            <td>Notes</td>
        </tr>
    </thead>
    <tbody>
        <tr>
            <td>PyTorch</td>
            <td>
                <a  href="https://github.com/PeterL1n/RobustVideoMatting/releases/download/v1.0.0/rvm_mobilenetv3.pth">rvm_mobilenetv3.pth</a><br>
                <a  href="https://github.com/PeterL1n/RobustVideoMatting/releases/download/v1.0.0/rvm_resnet50.pth">rvm_resnet50.pth</a>
            </td>
            <td>
                Official weights for PyTorch. <a href="documentation/inference.md#pytorch">Doc</a>
            </td>
        </tr>
        <tr>
            <td>TorchHub</td>
            <td>
                Nothing to Download.
            </td>
            <td>
                Easiest way to use our model in your PyTorch project. <a href="documentation/inference.md#torchhub">Doc</a>
            </td>
        </tr>
        <tr>
            <td>TorchScript</td>
            <td>
                <a  href="https://github.com/PeterL1n/RobustVideoMatting/releases/download/v1.0.0/rvm_mobilenetv3_fp32.torchscript">rvm_mobilenetv3_fp32.torchscript</a><br>
                <a  href="https://github.com/PeterL1n/RobustVideoMatting/releases/download/v1.0.0/rvm_mobilenetv3_fp16.torchscript">rvm_mobilenetv3_fp16.torchscript</a><br>
                <a  href="https://github.com/PeterL1n/RobustVideoMatting/releases/download/v1.0.0/rvm_resnet50_fp32.torchscript">rvm_resnet50_fp32.torchscript</a><br>
                <a  href="https://github.com/PeterL1n/RobustVideoMatting/releases/download/v1.0.0/rvm_resnet50_fp16.torchscript">rvm_resnet50_fp16.torchscript</a>
            </td>
            <td>
                If inference on mobile, consider export int8 quantized models yourself. <a href="documentation/inference.md#torchscript">Doc</a>
            </td>
        </tr>

Excerpt of 12,293 characters

Read on GitHub

Top contributors5 shown

PeterL1n
Peter Lin · United States
22
DefTruth
DefTruth · @xlite-dev, @vipshop · China
7
AK391
Ahsen Khaliq · Huggingface
3
dcyoung
1
lanreolokoba
1

Quality score

Would you bet a product on this? Bounded 0–100 and slow moving.

43/10043

Maintenance: 5%; How recently the default branch was pushed to
Releases: 10%; Recency and cadence of tagged releases
Community: 40%; Contributor breadth and bus factor
Issue backlog: 85%; Open issues measured against the size of the audience
Documentation: 100%; README depth, plus a homepage and topics
Licensing: 55%; How freely the code can be adopted

What to know before adopting

No commits in over a year
No release in over a year
Copyleft license — derivative works must be open-sourced
Derivative works must be released under the same licence.

Details

Created: 5 years ago
Last push: 2 years ago
Last release: 5 years ago
Release tag: v1.0.0
Releases in the last year: 0
Default branch: master
Size: 9.2k KB
Owner type: User
Owner location: New York, New York, US
Discovered via: topic:computer-vision
First indexed: 14 days ago
Last synced: 9 days ago

Top AI Repos — open-source AI, indexed and scored

PeterL1n/RobustVideoMatting

From the README

Top contributors5 shown

Quality score

What to know before adopting

Categories

Details

PeterL1n/RobustVideoMatting

From the README

Top contributors5 shown

Quality score

What to know before adopting

Categories

Details

Top AI Repos — open-source AI, indexed and scored

Trend

From the README

Top contributors5 shown

Quality score

What to know before adopting

Categories

Details

Trend

From the README

Top contributors5 shown

Quality score

What to know before adopting

Categories

Details