  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}}.
