  This paper proposes a novel deep learning-based video object matting method
that can achieve temporally coherent matting results. Its key component is an
attention-based temporal aggregation module that maximizes image matting
networks' strength for video matting networks. This module computes temporal
correlations for pixels adjacent to each other along the time axis in feature
space, which is robust against motion noises. We also design a novel loss term
to train the attention weights, which drastically boosts the video matting
performance. Besides, we show how to effectively solve the trimap generation
problem by fine-tuning a state-of-the-art video object segmentation network
with a sparse set of user-annotated keyframes. To facilitate video matting and
trimap generation networks' training, we construct a large-scale video matting
dataset with 80 training and 28 validation foreground video clips with
ground-truth alpha mattes. Experimental results show that our method can
generate high-quality alpha mattes for various videos featuring appearance
change, occlusion, and fast motion. Our code and dataset can be found at:
https://github.com/yunkezhang/TCVOM
