We propose a novel neural network approach, LARP (Learned Articulated Rigid body Physics), to model the dynamics of articulated human motion with contact. Our goal is to develop a faster and more convenient methodological alternative to traditional physics simulators for use in computer vision tasks such as human motion reconstruction from video. To that end we introduce a training procedure and model components that support the construction of a recurrent neural architecture to accurately simulate articulated rigid body dynamics. Our neural architecture supports features typically found in traditional physics simulators, such as modeling of joint motors, variable dimensions of body parts, contact between body parts and objects, and is an order of magnitude faster than traditional systems when multiple simulations are run in parallel. To demonstrate the value of LARP we use it as a drop-in replacement for a state of the art classical non-differentiable simulator in an existing video-based reconstruction framework and show comparative or better 3D human pose reconstruction accuracy.

本文针对传统物理模拟器在计算机视觉任务中应用的效率不足问题，提出了一种新颖的神经网络方法LARP，旨在实现更快、更便捷的模型。该方法能够高度准确地模拟关节刚体动力学，相比传统系统在并行运行多个模拟时速度提升了一个数量级，且在视频基础重建框架中展示了与先进的经典模拟器相当或更好的3D人类姿态重建精度。

用于关节3D人类姿态重建的学习神经物理模拟