Commercial Solution (商用方案) | Research Demo | Arxiv Preprint | Supplementary Video
Community | Code | PPM Benchmark | License | Acknowledgement | Citation | Contact
--- ## Commercial Solution (商用方案) Our commercial solution for portrait matting is coming! 我们的人像抠图商用方案来了！ ### Portrait Image Matting Solution (图片抠像方案) A **Single** Model! Only **7M**! Process **2K** resolution image with a **Fast** speed on common PCs or Mobiles! **单个**模型！大小仅为**7M**！可以在普通PC或移动设备上**快速**处理具有**2K**分辨率的图像！ Now you can try the Beta version of our **portrait image matting** online via [this website](https://sight-x.cn/portrait_matting). (As our current server for testing is hosted in China, your access may be delayed.) 现在，您可以通过[此网站](https://sight-x.cn/portrait_matting)在线使用我们的**图片抠像**测试版。
The commercial API interface for **portrait image matting** will be available soon.
用于**图片抠像**的商用API接口即将推出。
If you are interest in a **portrait image matting SDK**, please contact `bussiness@sight-x.com`.
如果您对**图片抠像SDK**感兴趣，请联系`bussiness@sight-x.com`。
Here are two example videos processed (frame independently) via our **portrait image matting** model:
我们**图片抠像**模型逐帧单独处理的两个示例视频:
### Portrait Video Matting Solution (视频抠像方案)
Stay tuned.
敬请期待。
---
## Research Demo
All the models behind the following demos are trained on the datasets mentioned in [our paper](https://arxiv.org/pdf/2011.11961.pdf).
### Portrait Image Matting
We provide an [online Colab demo](https://colab.research.google.com/drive/1GANpbKT06aEFiW-Ssx0DQnnEADcXwQG6?usp=sharing) for portrait image matting.
It allows you to upload portrait images and predict/visualize/download the alpha mattes.
### Portrait Video Matting
We provide two real-time portrait video matting demos based on WebCam. When using the demo, you can move the WebCam around at will.
If you have an Ubuntu system, we recommend you to try the [offline demo](demo/video_matting/webcam) to get a higher *fps*. Otherwise, you can access the [online Colab demo](https://colab.research.google.com/drive/1Pt3KDSc2q7WxFvekCnCLD8P0gBEbxm6J?usp=sharing).
We also provide an [offline demo](demo/video_matting/custom) that allows you to process custom videos.
## Community
We share some cool applications/extentions of MODNet built by the community.
- **WebGUI for Portrait Image Matting**
You can try [this WebGUI](https://www.gradio.app/hub/aliabd/modnet) (hosted on [Gradio](https://www.gradio.app/)) for portrait image matting from your browser without code!
- **Colab Demo of Bokeh (Blur Background)**
You can try [this Colab demo](https://colab.research.google.com/github/eyaler/avatars4all/blob/master/yarok.ipynb) (built by [@eyaler](https://github.com/eyaler)) to blur the backgroud based on MODNet!
- **ONNX Version of MODNet**
You can convert the pre-trained MODNet to an ONNX model by using [this code](onnx) (provided by [@manthan3C273](https://github.com/manthan3C273)). You can also try [this Colab demo](https://colab.research.google.com/drive/1P3cWtg8fnmu9karZHYDAtmm1vj1rgA-f?usp=sharing) for MODNet image matting (ONNX version).
- **TorchScript Version of MODNet**
You can convert the pre-trained MODNet to an TorchScript model by using [this code](torchscript) (provided by [@yarkable](https://github.com/yarkable)).
- **TensorRT Version of MODNet**
You can access [this Github repository](https://github.com/jkjung-avt/tensorrt_demos) to try the TensorRT version of MODNet (provided by [@jkjung-avt](https://github.com/jkjung-avt)).
There are some resources about MODNet from the community.
- [Video from What's AI YouTube Channel](https://youtu.be/rUo0wuVyefU)
- [Article from Louis Bouchard's Blog](https://www.louisbouchard.ai/remove-background/)
## Code
We provide the [code](src/trainer.py) of MODNet training iteration, including:
- **Supervised Training**: Train MODNet on a labeled matting dataset
- **SOC Adaptation**: Adapt a trained MODNet to an unlabeled dataset
In the code comments, we provide examples for using the functions.
## PPM Benchmark
The PPM benchmark will be released in a separate repository [PPM](https://github.com/ZHKKKe/PPM).
## License
All resources in this repository (code, models, demos, *etc.*) are released under the [Creative Commons Attribution NonCommercial ShareAlike 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode) license.
The license will be changed to allow commercial use after our paper is accepted.
## Acknowledgement
- We thank
[@eyaler](https://github.com/eyaler), [@manthan3C273](https://github.com/manthan3C273), [@yarkable](https://github.com/yarkable), [@jkjung-avt](https://github.com/jkjung-avt),
[the Gradio team](https://github.com/gradio-app/gradio), [What's AI YouTube Channel](https://www.youtube.com/channel/UCUzGQrN-lyyc0BWTYoJM_Sg), [Louis Bouchard's Blog](https://www.louisbouchard.ai),
for their contributions to this repository or their cool applications/extentions/resources of MODNet.
## Citation
If this work helps your research, please consider to cite:
```bibtex
@article{MODNet,
author = {Zhanghan Ke and Kaican Li and Yurou Zhou and Qiuhua Wu and Xiangyu Mao and Qiong Yan and Rynson W.H. Lau},
title = {Is a Green Screen Really Necessary for Real-Time Portrait Matting?},
journal={ArXiv},
volume={abs/2011.11961},
year = {2020},
}
```
## Contact
This repository is currently maintained by Zhanghan Ke ([@ZHKKKe](https://github.com/ZHKKKe)).
For commercial questions, please contact `bussiness@sight-x.com`.
For research questions, please contact `kezhanghan@outlook.com`.