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MODNet - Custom Portrait Video Matting Demo

This is a MODNet portrait video matting demo that allows you to process custom videos.

1. Requirements

The basic requirements for this demo are:

• Ubuntu System
• Python 3+

2. Introduction

We use ~400 unlabeled video clips (divided into ~50,000 frames) downloaded from the internet to perform SOC to adapt MODNet to the video domain. Nonetheless, due to insufficient labeled training data (~3k labeled foregrounds), our model may still make errors in portrait semantics estimation under challenging scenes. Besides, this demo does not currently support the OFD trick.

For a better experience, please make sure your videos satisfy:

• the portrait and background are distinguishable, i.e., are not similar
• captured in soft and bright ambient lighting
• the contents do not move too fast

3. Run Demo

We recommend creating a new conda virtual environment to run this demo, as follow:

1. Clone the MODNet repository:

   git clone https://github.com/ZHKKKe/MODNet.git
   cd MODNet
   

2. Download the pre-trained model from this link and put it into the folder MODNet/pretrained/.

3. Create a conda virtual environment named modnet (if it doesn't exist) and activate it. Here we use python=3.6 as an example:

   conda create -n modnet python=3.6
   source activate modnet
   

4. Install the required python dependencies (please make sure your CUDA version is supported by the PyTorch version installed):

   pip install -r demo/video_matting/custom/requirements.txt
   

5. Execute the main code:

   python -m demo.video_matting.custom.run --video YOUR_VIDEO_PATH
   

   where YOUR_VIDEO_PATH is the specific path of your video.
   There are some optional arguments:

   • --result-type (default=fg) : fg - save the alpha matte; fg - save the foreground
   • --fps (default=30) : fps of the result video