用物理映射提升多人交互动作生成的真实感
PhysiInter: Integrating Physical Mapping for High-Fidelity Human Interaction Generation
- 在物理仿真环境中实现动作模仿，确保生成动作符合真实物理约束
- 多人体交互场景下，物理保真度提升3%-89%
- 适合需要高真实感动作生成的影视、游戏和虚拟现实应用
随着动作捕捉和生成式人工智能的发展，利用大规模动捕数据训练生成模型以合成多样且逼真的身体运动已成为重要研究方向。然而，现有动作捕捉技术与生成模型常忽视物理约束，导致出现穿模、滑移、漂浮等伪影，尤其在多人交互场景中问题更严重。为此，本文提出在人体交互生成全流程中引入物理映射机制：在基于物理的仿真环境中进行动作模仿，将目标动作投影至物理上合理空间，并调整生成动作以满足真实物理规律，同时保持语义一致性。该映射不仅提升了动捕数据质量，也直接指导生成动作的后处理。针对多人交互特性，提出运动一致性（MC）与基于标记的交互（MI）损失函数，显著提升模型表现。实验表明，本方法在生成动作质量上取得显著进步，物理保真度提升3%-89%。
原文摘要 · Abstract (English)
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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