Motion Guided 3D Pose Estimation from Videos

Understand

We propose a new loss function, called motion loss, for the problem of monocular 3D Human pose estimation from 2D pose.

• In computing motion loss, a simple yet effective representation for keypoint motion, called pairwise motion encoding, is introduced.
• We design a new graph convolutional network architecture, U-shaped GCN (UGCN).
• It captures both short-term and long-term motion information to fully leverage the additional supervision from the motion loss.