This paper presents a motion data augmentation scheme incorporating motion
synthesis encouraging diversity and motion correction imposing physical
plausibility. This motion synthesis consists of our modified Variational
AutoEncoder (VAE) and Inverse Kinematics (IK). In this VAE, our proposed
sampling-near-samples method generates various valid motions even with
insufficient training motion data. Our IK-based motion synthesis method allows
us to generate a variety of motions semi-automatically. Since these two schemes
generate unrealistic artifacts in the synthesized motions, our motion
correction rectifies them. This motion correction scheme consists of imitation
learning with physics simulation and subsequent motion debiasing. For this
imitation learning, we propose the PD-residual force that significantly
accelerates the training process. Furthermore, our motion debiasing
successfully offsets the motion bias induced by imitation learning to maximize
the effect of augmentation. As a result, our method outperforms previous
noise-based motion augmentation methods by a large margin on both Recurrent
Neural Network-based and Graph Convolutional Network-based human motion
prediction models. The code is available at
https://github.com/meaten/MotionAug.Comment: Accepted at CVPR202