Semantic human matting aims to estimate the per-pixel opacity of the
foreground human regions. It is quite challenging and usually requires user
interactive trimaps and plenty of high quality annotated data. Annotating such
kind of data is labor intensive and requires great skills beyond normal users,
especially considering the very detailed hair part of humans. In contrast,
coarse annotated human dataset is much easier to acquire and collect from the
public dataset. In this paper, we propose to use coarse annotated data coupled
with fine annotated data to boost end-to-end semantic human matting without
trimaps as extra input. Specifically, we train a mask prediction network to
estimate the coarse semantic mask using the hybrid data, and then propose a
quality unification network to unify the quality of the previous coarse mask
outputs. A matting refinement network takes in the unified mask and the input
image to predict the final alpha matte. The collected coarse annotated dataset
enriches our dataset significantly, allows generating high quality alpha matte
for real images. Experimental results show that the proposed method performs
comparably against state-of-the-art methods. Moreover, the proposed method can
be used for refining coarse annotated public dataset, as well as semantic
segmentation methods, which reduces the cost of annotating high quality human
data to a great extent.

本文提出了一种使用粗略标注数据和精细标注数据的混合数据来提高端到端语义人物抠图的方法。通过使用一个掩模预测网络来估计粗略的语义掩模，再利用一个掩模优化网络来统一之前粗略掩模的质量。最终使用抠图细化网络和输入图片来预测最终的阿尔法码。该方法在细化公共 coarse annotated dataset，对比其他最新方法表现一致。