Efficient Video Matting on Human Video Clips for Real-Time Application

This paper presents an efficient and effective matting framework for human video clips. To alleviate the inefficiency problem in existing models, we propose using a refiner dedicated to error-prone regions, and reduce the computation at higher resolutions, so the proposed framework can achieve real-time performance for 1080p 60fps videos. Also, with the recurrent architecture, our model is aware of temporal information and produces temporally more consistent matting results compared to models processing each frame individually. Moreover, it contains a module for capturing semantic information. That makes our model easy to use without troublesome setup, such as annotating trimaps or other additional inputs. Experiments show that our proposed method outperforms previous matting methods, and reaches the state of the art on the VideoMatte240K dataset.