Human Motion Prediction under Unexpected Perturbation

• URL: http://arxiv.org/abs/2403.15891v1
• Date: Sat, 23 Mar 2024 17:17:08 GMT
• Title: Human Motion Prediction under Unexpected Perturbation
• Authors: Jiangbei Yue, Baiyi Li, Julien Pettré, Armin Seyfried, He Wang,
• Abstract summary: We investigate a new task in human motion prediction, which is predicting motions under unexpected physical perturbation. This task involves predicting less controlled, unpremeditated and pure reactive motions in response to external impact. We propose a new method capitalizing differential physics and deep neural networks, leading to an explicit Latent Differential Physics model.
• Score: 9.464047853301603
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We investigate a new task in human motion prediction, which is predicting motions under unexpected physical perturbation potentially involving multiple people. Compared with existing research, this task involves predicting less controlled, unpremeditated and pure reactive motions in response to external impact and how such motions can propagate through people. It brings new challenges such as data scarcity and predicting complex interactions. To this end, we propose a new method capitalizing differential physics and deep neural networks, leading to an explicit Latent Differential Physics (LDP) model. Through experiments, we demonstrate that LDP has high data efficiency, outstanding prediction accuracy, strong generalizability and good explainability. Since there is no similar research, a comprehensive comparison with 11 adapted baselines from several relevant domains is conducted, showing LDP outperforming existing research both quantitatively and qualitatively, improving prediction accuracy by as much as 70%, and demonstrating significantly stronger generalization.

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