PhysiInter: Integrating Physical Mapping for High-Fidelity Human Interaction Generation
Journal:
arXiv
Published Date:
Jun 9, 2025
Abstract
Driven by advancements in motion capture and generative artificial
intelligence, leveraging large-scale MoCap datasets to train generative models
for synthesizing diverse, realistic human motions has become a promising
research direction. However, existing motion-capture techniques and generative
models often neglect physical constraints, leading to artifacts such as
interpenetration, sliding, and floating. These issues are exacerbated in
multi-person motion generation, where complex interactions are involved. To
address these limitations, we introduce physical mapping, integrated throughout
the human interaction generation pipeline. Specifically, motion imitation
within a physics-based simulation environment is used to project target motions
into a physically valid space. The resulting motions are adjusted to adhere to
real-world physics constraints while retaining their original semantic meaning.
This mapping not only improves MoCap data quality but also directly informs
post-processing of generated motions. Given the unique interactivity of
multi-person scenarios, we propose a tailored motion representation framework.
Motion Consistency (MC) and Marker-based Interaction (MI) loss functions are
introduced to improve model performance. Experiments show our method achieves
impressive results in generated human motion quality, with a 3%-89% improvement
in physical fidelity. Project page http://yw0208.github.io/physiinter
