  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
\url{https://pare.is.tue.mpg.de/}}
