  Despite the significant progress made by deep learning in natural image
matting, there has been so far no representative work on deep learning for
video matting due to the inherent technical challenges in reasoning temporal
domain and lack of large-scale video matting datasets. In this paper, we
propose a deep learning-based video matting framework which employs a novel and
effective spatio-temporal feature aggregation module (ST-FAM). As optical flow
estimation can be very unreliable within matting regions, ST-FAM is designed to
effectively align and aggregate information across different spatial scales and
temporal frames within the network decoder. To eliminate frame-by-frame trimap
annotations, a lightweight interactive trimap propagation network is also
introduced. The other contribution consists of a large-scale video matting
dataset with groundtruth alpha mattes for quantitative evaluation and
real-world high-resolution videos with trimaps for qualitative evaluation.
Quantitative and qualitative experimental results show that our framework
significantly outperforms conventional video matting and deep image matting
methods applied to video in presence of multi-frame temporal information.
