PhysiInter: Integrating Physical Mapping for High-Fidelity Human Interaction Generation

• URL: http://arxiv.org/abs/2506.07456v1
• Date: Mon, 09 Jun 2025 06:04:49 GMT
• Title: PhysiInter: Integrating Physical Mapping for High-Fidelity Human Interaction Generation
• Authors: Wei Yao, Yunlian Sun, Chang Liu, Hongwen Zhang, Jinhui Tang,
• Abstract summary: We introduce physical mapping, integrated throughout the human interaction generation pipeline.<n>Specifically, motion imitation within a physics-based simulation environment is used to project target motions into a physically valid space.<n>Experiments show our method achieves impressive results in generated human motion quality, with a 3%-89% improvement in physical fidelity.
• Score: 35.563978243352764
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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

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