  Automatic portrait video matting is an under-constrained problem. Most
state-of-the-art methods only exploit the semantic information and process each
frame individually. Their performance is compromised due to the lack of
temporal information between the frames. To solve this problem, we propose the
context motion network to leverage semantic information and motion information.
To capture the motion information, we estimate the optical flow and design a
context-motion updating operator to integrate features between frames
recurrently. Our experiments show that our network outperforms state-of-the-art
matting methods significantly on the Video240K SD dataset.
