Despite significant progress, we show that state of the art 3D human pose and
shape estimation methods remain sensitive to partial occlusion and can produce
dramatically wrong predictions although much of the body is observable. To
address this, we introduce a soft attention mechanism, called the Part
Attention REgressor (PARE), that learns to predict body-part-guided attention
masks. We observe that state-of-the-art methods rely on global feature
representations, making them sensitive to even small occlusions. In contrast,
PARE's part-guided attention mechanism overcomes these issues by exploiting
information about the visibility of individual body parts while leveraging
information from neighboring body-parts to predict occluded parts. We show
qualitatively that PARE learns sensible attention masks, and quantitative
evaluation confirms that PARE achieves more accurate and robust reconstruction
results than existing approaches on both occlusion-specific and standard
benchmarks. The code and data are available for research purposes at {\small
https://pare.is.tue.mpg.de/}

本文提出一种软注意机制 (PARE)，通过学习预测部位引导的注意掩蔽层来适应部分遮挡，以提高对 3D 人体姿态估计和形状估计的精度和鲁棒性。实验证明，PARE 比现有方法在遮挡和标准基准测试上具有更精确和更稳健的重建结果。