  Although significant progress has been achieved on monocular maker-less human
motion capture in recent years, it is still hard for state-of-the-art methods
to obtain satisfactory results in occlusion scenarios. There are two main
reasons: the one is that the occluded motion capture is inherently ambiguous as
various 3D poses can map to the same 2D observations, which always results in
an unreliable estimation. The other is that no sufficient occluded human data
can be used for training a robust model. To address the obstacles, our key-idea
is to employ non-occluded human data to learn a joint-level spatial-temporal
motion prior for occluded human with a self-supervised strategy. To further
reduce the gap between synthetic and real occlusion data, we build the first 3D
occluded motion dataset~(OcMotion), which can be used for both training and
testing. We encode the motions in 2D maps and synthesize occlusions on
non-occluded data for the self-supervised training. A spatial-temporal layer is
then designed to learn joint-level correlations. The learned prior reduces the
ambiguities of occlusions and is robust to diverse occlusion types, which is
then adopted to assist the occluded human motion capture. Experimental results
show that our method can generate accurate and coherent human motions from
occluded videos with good generalization ability and runtime efficiency. The
dataset and code are publicly available at
\url{https://github.com/boycehbz/CHOMP}.
