MotionAug: Augmentation with Physical Correction for Human Motion Prediction
Takahiro Maeda, Norimichi Ukita

TL;DR
MotionAug introduces a novel augmentation method combining motion synthesis, physical correction, and motion debiasing to improve human motion prediction, outperforming previous noise-based augmentation techniques.
Contribution
The paper proposes a new motion augmentation framework integrating a modified VAE, inverse kinematics, and physics-based motion correction with imitation learning and debiasing.
Findings
Outperforms previous noise-based augmentation methods.
Effective with both RNN and GCN-based prediction models.
Significantly accelerates training with PD-residual force.
Abstract
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…
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Taxonomy
TopicsHuman Pose and Action Recognition · Human Motion and Animation · Gait Recognition and Analysis
