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.

我们开发了一种方法来明确建模隐藏关节推理过程以显着增强有或无遮挡的人体姿势估计，将任务分为两个子任务：可见关键点检测和遮挡关键点推理，并提出了深度监督编码器蒸馏和骨架引导人体形状拟合来训练我们的模型，实验证明从遮挡中明确学习可以提高人体姿势估计的性能。