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This paper introduces a novel human pose estimation approach using sparseinertial sensors, addressing the shortcomings of previous methods reliant onsynthetic data. It leverages a diverse array of real inertial motion capturedata from different skeleton formats to improve motion diversity and modelgeneralization. This method features two innovative components: apseudo-velocity regression model for dynamic motion capture with inertialsensors, and a part-based model dividing the body and sensor data into threeregions, each focusing on their unique characteristics. The approachdemonstrates superior performance over state-of-the-art models across fivepublic datasets, notably reducing pose error by 19\% on the DIP-IMU dataset,thus representing a significant improvement in inertial sensor-based human poseestimation. We will make the implementation of our model available for publicuse.

Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial Sensors