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

Automatic Portrait Video Matting via Context Motion Network