Monocular Human Pose and Shape Reconstruction using Part Differentiable Rendering

Understand

Superior human pose and shape reconstruction from monocular images depends on removing the ambiguities caused by occlusions and shape variance.

• Recent works succeed in regression-based methods which estimate parametric models directly through a deep neural network supervised by 3D ground truth.
• However, 3D ground truth is neither in abundance nor can efficiently be obtained.
• In this paper, we introduce body part segmentation as critical supervision.