Synthetic Training for Accurate 3D Human Pose and Shape Estimation in the Wild

Understand

This paper addresses the problem of monocular 3D human shape and pose estimation from an RGB image.

• Despite great progress in this field in terms of pose prediction accuracy, state-of-the-art methods often predict inaccurate body shapes.
• We suggest that this is primarily due to the scarcity of in-the-wild training data with diverse and accurate body shape labels.
• Thus, we propose STRAPS (Synthetic Training for Real Accurate Pose and Shape), a system that utilises proxy representations, such as silhouettes and 2D joints, as inputs to a shape and pose regression neural network, which is trained with synthetic training data (generated on-the-fly during training using the SMPL statistical body model) to overcome data scarcity.