  Human body part segmentation refers to the task of predicting the semantic
segmentation mask for each body part. Fully supervised body part segmentation
methods achieve good performances but require an enormous amount of effort to
annotate part masks for training. In contrast to high annotation costs needed
for a limited number of part mask annotations, a large number of weak labels
such as poses and full body masks already exist and contain relevant
information. Motivated by the possibility of using existing weak labels, we
propose the first weakly supervised body part segmentation framework. The core
idea is first converting the sparse weak labels such as keypoints to the
initial estimate of body part masks, and then iteratively refine the part mask
predictions. We name the initial part masks estimated from poses the "part
priors." With sufficient extra weak labels, our weakly supervised framework
achieves a comparable performance (62.0% mIoU) to the fully supervised method
(63.6% mIoU) on the Pascal-Person-Part dataset. Furthermore, in the extended
semi-supervised setting, the proposed framework outperforms the state-of-art
methods. Moreover, we extend our proposed framework to other
keypoint-supervised part segmentation tasks such as face parsing.
