This paper introduces a novel human pose estimation approach using sparse inertial sensors, addressing the shortcomings of previous methods reliant on synthetic data. It leverages a diverse array of real inertial motion capture data from different skeleton formats to improve motion diversity and model generalization. This method features two innovative components: a pseudo-velocity regression model for dynamic motion capture with inertial sensors, and a part-based model dividing the body and sensor data into three regions, each focusing on their unique characteristics. The approach demonstrates superior performance over state-of-the-art models across five public datasets, notably reducing pose error by 19\% on the DIP-IMU dataset, thus representing a significant improvement in inertial sensor-based human pose estimation. Our codes are available at {\url{https://github.com/dx118/dynaip}}.

翻译：本文提出了一种利用稀疏惯性传感器进行人体姿态估计的新方法，以解决以往依赖合成数据的方法存在的缺陷。该方法利用来自不同骨骼格式的多种真实惯性运动捕捉数据，以提高运动多样性和模型泛化能力。该技术包含两个创新组件：一个用于惯性传感器动态运动捕捉的伪速度回归模型，以及一个将身体和传感器数据划分为三个区域的部位模型，每个区域聚焦于其独特特征。该方法在五个公开数据集上展现出优于现有最优模型的性能，在DIP-IMU数据集上姿态误差降低了19%，从而代表了基于惯性传感器的人体姿态估计的显著改进。我们的代码可在 {\url{https://github.com/dx118/dynaip}} 获取。