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:
this https URL

该论文提出了一种基于深度学习的新型视频对象抠图方法，能够实现时间上连贯的抠图效果，并使用注意力机制的时间聚合模块在特征空间中计算相邻像素沿时间轴的时序相关性，同时利用带有稀疏用户注释关键帧的先进视频对象分割网络进行细调以解决抠图问题。实验结果表明，该方法能够生成适用于各种视频的高质量阿尔法抠图。