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
this https URL

本文提出了一种运动数据增强方案，该方案融合了具有多样性的运动合成和强制物理合理性的运动修正。该方案使用了修改后的 VAE 和 IK 进行运动合成，使用物理仿真的模仿学习和后续运动去偏置等方式进行了运动校正。该方法在基于循环神经网络和图卷积网络的人体运动预测模型中比以往的基于噪声的运动增强方法表现更佳。