  Occlusion poses a great threat to monocular multi-person 3D human pose
estimation due to large variability in terms of the shape, appearance, and
position of occluders. While existing methods try to handle occlusion with pose
priors/constraints, data augmentation, or implicit reasoning, they still fail
to generalize to unseen poses or occlusion cases and may make large mistakes
when multiple people are present. Inspired by the remarkable ability of humans
to infer occluded joints from visible cues, we develop a method to explicitly
model this process that significantly improves bottom-up multi-person human
pose estimation with or without occlusions. First, we split the task into two
subtasks: visible keypoints detection and occluded keypoints reasoning, and
propose a Deeply Supervised Encoder Distillation (DSED) network to solve the
second one. To train our model, we propose a Skeleton-guided human Shape
Fitting (SSF) approach to generate pseudo occlusion labels on the existing
datasets, enabling explicit occlusion reasoning. Experiments show that
explicitly learning from occlusions improves human pose estimation. In
addition, exploiting feature-level information of visible joints allows us to
reason about occluded joints more accurately. Our method outperforms both the
state-of-the-art top-down and bottom-up methods on several benchmarks.
