  In this paper, we tackle the problem of human de-occlusion which reasons
about occluded segmentation masks and invisible appearance content of humans.
In particular, a two-stage framework is proposed to estimate the invisible
portions and recover the content inside. For the stage of mask completion, a
stacked network structure is devised to refine inaccurate masks from a general
instance segmentation model and predict integrated masks simultaneously.
Additionally, the guidance from human parsing and typical pose masks are
leveraged to bring prior information. For the stage of content recovery, a
novel parsing guided attention module is applied to isolate body parts and
capture context information across multiple scales. Besides, an Amodal Human
Perception dataset (AHP) is collected to settle the task of human de-occlusion.
AHP has advantages of providing annotations from real-world scenes and the
number of humans is comparatively larger than other amodal perception datasets.
Based on this dataset, experiments demonstrate that our method performs over
the state-of-the-art techniques in both tasks of mask completion and content
recovery. Our AHP dataset is available at
\url{https://sydney0zq.github.io/ahp/}.
