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.

翻译：本文提出了一种运动数据增强方案，融合了鼓励多样性的运动合成与施加物理合理性的运动修正。该运动合成包含改进的变分自编码器（VAE）和逆运动学（IK）。在此VAE中，我们提出的样本邻近采样方法即使在训练运动数据不足的情况下也能生成各种有效运动。基于IK的运动合成方法使我们能够半自动地生成多种运动。由于这两种方案会在合成运动中产生不真实的伪影，我们的运动修正予以矫正。该运动修正方案由基于物理模拟的模仿学习及随后的运动去偏构成。针对此模仿学习，我们提出了PD残差力，极大地加速了训练过程。此外，我们的运动去偏成功抵消了模仿学习引入的运动偏差，从而最大化增强效果。最终，在基于循环神经网络和基于图卷积网络的人体运动预测模型上，我们的方法以较大优势超越了以往基于噪声的运动增强方法。代码可在https://github.com/meaten/MotionAug获取。