数据驱动的真实人体关节约束仿真方法
Data-Driven Approach to Simulating Realistic Human Joint Constraints
摘要：
模拟真实的人体关节极限对于涉及物理人机交互的应用是重要的。然而，设置适当的人体关节限制是具有挑战性的，因为它是姿势相关的：关节运动的范围取决于其他骨骼的位置。介绍了一种在物理仿真中精确模拟人体关节极限的新技术。我们建议学习一个隐式方程来表示从真实人体数据的有效人体关节配置的边界。隐式方程中的函数由完全连接的神经网络表示，其梯度可以通过反向传播有效地计算。使用梯度，我们可以通过物理引擎中的约束力或优化问题中的约束来有效地实施现实的人体关节限制。
英文摘要：
Modeling realistic human joint limits is important for applications involving physical human-robot interaction. However, setting appropriate human joint limits is challenging because it is pose-dependent: the range of joint motion varies depending on the positions of other bones. The paper introduces a new technique to accurately simulate human joint limits in physics simulation. We propose to learn an implicit equation to represent the boundary of valid human joint configurations from real human data. The function in the implicit equation is represented by a fully connected neural network whose gradients can be efficiently computed via back-propagation. Using gradients, we can efficiently enforce realistic human joint limits through constraint forces in a physics engine or as constraints in an optimization problem.
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。
