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We propose a new loss function, called motion loss, for the problem ofmonocular 3D Human pose estimation from 2D pose. In computing motion loss, asimple yet effective representation for keypoint motion, called pairwise motionencoding, is introduced. We design a new graph convolutional networkarchitecture, U-shaped GCN (UGCN). It captures both short-term and long-termmotion information to fully leverage the additional supervision from the motionloss. We experiment training UGCN with the motion loss on two large scalebenchmarks: Human3.6M and MPI-INF-3DHP. Our model surpasses otherstate-of-the-art models by a large margin. It also demonstrates strong capacityin producing smooth 3D sequences and recovering keypoint motion.

Motion Guided 3D Pose Estimation from Videos