State Key Laboratory of Tribology in Advanced Equipment, Department of Mechanical Engineering, Tsinghua University, 100084, Beijing, China.
Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipments and Control, Department of Mechanical Engineering, Tsinghua University, 100084, Beijing, China.
Nat Commun. 2024 Mar 18;15(1):2423. doi: 10.1038/s41467-024-46662-5.
Inertial Measurement Unit-based methods have great potential in capturing motion in large-scale and complex environments with many people. Sparse Inertial Measurement Unit-based methods have more research value due to their simplicity and flexibility. However, improving the computational efficiency and reducing latency in such methods are challenging. In this paper, we propose Fast Inertial Poser, which is a full body motion estimation deep neural network based on 6 inertial measurement units considering body parameters. We design a network architecture based on recurrent neural networks according to the kinematics tree. This method introduces human body shape information by the causality of observations and eliminates the dependence on future frames. During the estimation of joint positions, the upper body and lower body are estimated using separate network modules independently. Then the joint rotation is obtained through a well-designed single-frame kinematics inverse solver. Experiments show that the method can greatly improve the inference speed and reduce the latency while ensuring the reconstruction accuracy compared with previous methods. Fast Inertial Poser runs at 65 fps with 15 ms latency on an embedded computer, demonstrating the efficiency of the model.
基于惯性测量单元的方法在捕捉大型复杂环境中多人运动方面具有很大的潜力。由于其简单性和灵活性，稀疏惯性测量单元方法具有更多的研究价值。然而，提高此类方法的计算效率和降低延迟是具有挑战性的。在本文中，我们提出了 Fast Inertial Poser，这是一种基于 6 个惯性测量单元的全身运动估计深度神经网络，考虑了身体参数。我们根据运动学树设计了一种基于递归神经网络的网络架构。该方法通过观察的因果关系引入人体形状信息，消除了对未来帧的依赖。在关节位置估计过程中，上身和下身使用独立的网络模块分别进行估计。然后通过精心设计的单帧运动学逆解算器获得关节旋转。实验表明，与以前的方法相比，该方法在保证重建精度的同时，可以大大提高推断速度并降低延迟。Fast Inertial Poser 在嵌入式计算机上以 65 fps 的速度运行，延迟为 15 ms，证明了该模型的效率。
