Online Solution (在线方案) | Research Demo | Arxiv Preprint | Supplementary Video
Community | Code | PPM Benchmark | License | Acknowledgement | Citation | Contact
News: We create a repository for our new model MODNet-V that focuses on faster and better portrait video matting.
News: The PPM-100 benchmark is released in this repository.
The online solution for portrait matting is coming!
人像抠图在线方案发布了！
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 our portrait image matting online via this website.
现在，您可以通过此网站在线使用我们的图片抠像功能。
All the models behind the following demos are trained on the datasets mentioned in our paper.
We provide an online Colab demo for portrait image matting.
It allows you to upload portrait images and predict/visualize/download the alpha mattes.
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 to get a higher fps. Otherwise, you can access the online Colab demo.
We also provide an offline demo that allows you to process custom videos.
We share some cool applications/extentions of MODNet built by the community.
-
WebGUI for Portrait Image Matting
You can try this WebGUI (hosted on Gradio) for portrait image matting from your browser without code! -
Colab Demo of Bokeh (Blur Background)
You can try this Colab demo (built by @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 (provided by @manthan3C273). You can also try this Colab demo 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 (provided by @yarkable). -
TensorRT Version of MODNet
You can access this Github repository to try the TensorRT version of MODNet (provided by @jkjung-avt).
There are some resources about MODNet from the community.
We provide the code 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.
The PPM benchmark is released in a separate repository PPM.
All resources in this repository (code, models, demos, etc.) are released under the Creative Commons Attribution NonCommercial ShareAlike 4.0 license.
The license will be changed to allow commercial use after our paper is accepted.
- We thank
@eyaler, @manthan3C273, @yarkable, @jkjung-avt,
the Gradio team, What's AI YouTube Channel, Louis Bouchard's Blog,
for their contributions to this repository or their cool applications/extentions/resources of MODNet.
If this work helps your research, please consider to cite:
@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},
}This repository is currently maintained by Zhanghan Ke (@ZHKKKe).
For questions, please contact kezhanghan@outlook.com.