2017/09/25 by Yifeng Jiang, C. Karen Liu, Jiang, Yifeng +1
Computer Science · Engineering · #3D Shape Modeling and Analysis #FOS: Computer and information sciences #Human Motion and Animation #Human Pose and Action Recognition #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.1709.08685
openalex publication_date 2017/09/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.