Supervised deep learning with pixel-wise training labels has great successes
on multi-person part segmentation. However, data labeling at pixel-level is
very expensive. To solve the problem, people have been exploring to use
synthetic data to avoid the data labeling. Although it is easy to generate
labels for synthetic data, the results are much worse compared to those using
real data and manual labeling. The degradation of the performance is mainly due
to the domain gap, i.e., the discrepancy of the pixel value statistics between
real and synthetic data. In this paper, we observe that real and synthetic
humans both have a skeleton (pose) representation. We found that the skeletons
can effectively bridge the synthetic and real domains during the training. Our
proposed approach takes advantage of the rich and realistic variations of the
real data and the easily obtainable labels of the synthetic data to learn
multi-person part segmentation on real images without any human-annotated
labels. Through experiments, we show that without any human labeling, our
method performs comparably to several state-of-the-art approaches which require
human labeling on Pascal-Person-Parts and COCO-DensePose datasets. On the other
hand, if part labels are also available in the real-images during training, our
method outperforms the supervised state-of-the-art methods by a large margin.
We further demonstrate the generalizability of our method on predicting novel
keypoints in real images where no real data labels are available for the novel
keypoints detection. Code and pre-trained models are available at
this https URL

本文提出了一种无需使用人工注释标签，仅仅利用丰富真实数据和易获得合成数据标签的方法，通过姿态表示有效地建立了合成和真实域之间的桥梁，以学习在真实图像上进行多人身体部位分割的方法，并显示该方法表现出与需人工标注标签的现有方法可媲美的效果。