Causal Biomechanical Dependencies for Physically Consistent Locomotion Forecasting

Hui-Yun Deng, Chia-Yun Chiang, Yen-Chen Chen, Yu-Hui Huang

Keywords: Human Locomotion Forecasting, Causal Discovery, Biomechanical Priors, Physical Plausibility, Cross-dataset Generalization.

Abstract: Human motion prediction often lacks physical plausibility and degrades under cross-dataset setting. Focusing on human locomotion, we propose an approach that incorporates musculoskeletal dynamics via PCMCI-based causal discovery. By identifying phase-specific dependencies between muscle activations and joint moments in walking motions, we inject physical inductive biases into a spatio-temporal Transformer. Experiments show that PCMCI-guided supervision improves skeletal consistency and stability over direct regression. Counterfactual tests further demonstrate that these causal priors maintain global coordination under perturbations. These results suggest that capturing transferable physical dependencies is essential for biologically plausible motion forecasting.

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Submission Number: 9