Human Motion Prediction under Unexpected Perturbation
Jiangbei Yue Baiyi Li Julien Pettré Armin Seyfried He Wang
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.