  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. We experiment training UGCN with the motion loss on two large scale
benchmarks: Human3.6M and MPI-INF-3DHP. Our model surpasses other
state-of-the-art models by a large margin. It also demonstrates strong capacity
in producing smooth 3D sequences and recovering keypoint motion.
