Skip to yearly menu bar Skip to main content


Poster

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

Yu Zhang · Songpengcheng Xia · Lei Chu · Jiarui Yang · Qi Wu · Ling Pei

Arch 4A-E Poster #167
[ ] [ Paper PDF ]
[ Poster
Wed 19 Jun 10:30 a.m. PDT — noon PDT

Abstract:

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 https://github.com/dx118/dynaip

Chat is not available.