  The standard approach to image instance segmentation is to perform the object
detection first, and then segment the object from the detection bounding-box.
More recently, deep learning methods like Mask R-CNN perform them jointly.
However, little research takes into account the uniqueness of the "human"
category, which can be well defined by the pose skeleton. Moreover, the human
pose skeleton can be used to better distinguish instances with heavy occlusion
than using bounding-boxes. In this paper, we present a brand new pose-based
instance segmentation framework for humans which separates instances based on
human pose, rather than proposal region detection. We demonstrate that our
pose-based framework can achieve better accuracy than the state-of-art
detection-based approach on the human instance segmentation problem, and can
moreover better handle occlusion. Furthermore, there are few public datasets
containing many heavily occluded humans along with comprehensive annotations,
which makes this a challenging problem seldom noticed by researchers.
Therefore, in this paper we introduce a new benchmark "Occluded Human
(OCHuman)", which focuses on occluded humans with comprehensive annotations
including bounding-box, human pose and instance masks. This dataset contains
8110 detailed annotated human instances within 4731 images. With an average
0.67 MaxIoU for each person, OCHuman is the most complex and challenging
dataset related to human instance segmentation. Through this dataset, we want
to emphasize occlusion as a challenging problem for researchers to study.
