面向物体引导的人-人协同操作的稳定性驱动运动生成
Stability-Driven Motion Generation for Object-Guided Human-Human Co-Manipulation
AI总结：
本文提出一种流匹配框架，通过物体 affordance 和空间配置生成自然且有效的协同操作运动，结合对抗性交互先验和稳定性驱动模拟，提升接触精度和交互稳定性。
摘要：
协同操作需要多个人类同步动作以共享对象，确保合理交互、自然姿势和稳定状态。然而，现有方法多针对单人场景或未考虑负载动态。本文提出一种流匹配框架，通过生成模型从物体 affordance 和空间配置中提取显式操作策略，引导运动流向成功操作。同时设计对抗性交互先验促进自然个体姿势和真实人际交互，结合稳定性驱动模拟通过采样优化和向量场回归提升操作效果。实验表明，本文方法在接触精度、穿透率和分布保真度上优于现有基线。代码见 https://github.com/boycehbz/StaCOM。
英文摘要：
Co-manipulation requires multiple humans to synchronize their motions with a shared object while ensuring reasonable interactions, maintaining natural poses, and preserving stable states. However, most existing motion generation approaches are designed for single-character scenarios or fail to account for payload-induced dynamics. In this work, we propose a flow-matching framework that ensures the generated co-manipulation motions align with the intended goals while maintaining naturalness and effectiveness. Specifically, we first introduce a generative model that derives explicit manipulation strategies from the object's affordance and spatial configuration, which guide the motion flow toward successful manipulation. To improve motion quality, we then design an adversarial interaction prior that promotes natural individual poses and realistic inter-person interactions during co-manipulation. In addition, we also incorporate a stability-driven simulation into the flow matching process, which refines unstable interaction states through sampling-based optimization and directly adjusts the vector field regression to promote more effective manipulation. The experimental results demonstrate that our method achieves higher contact accuracy, lower penetration, and better distributional fidelity compared to state-of-the-art human-object interaction baselines. The code is available at https://github.com/boycehbz/StaCOM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
