  Human body part parsing, or human semantic part segmentation, is fundamental
to many computer vision tasks. In conventional semantic segmentation methods,
the ground truth segmentations are provided, and fully convolutional networks
(FCN) are trained in an end-to-end scheme. Although these methods have
demonstrated impressive results, their performance highly depends on the
quantity and quality of training data. In this paper, we present a novel method
to generate synthetic human part segmentation data using easily-obtained human
keypoint annotations. Our key idea is to exploit the anatomical similarity
among human to transfer the parsing results of a person to another person with
similar pose. Using these estimated results as additional training data, our
semi-supervised model outperforms its strong-supervised counterpart by 6 mIOU
on the PASCAL-Person-Part dataset, and we achieve state-of-the-art human
parsing results. Our approach is general and can be readily extended to other
object/animal parsing task assuming that their anatomical similarity can be
annotated by keypoints. The proposed model and accompanying source code are
available at https://github.com/MVIG-SJTU/WSHP
