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Leveraging wearable devices for motion reconstruction has emerged as aneconomical and viable technique. Certain methodologies employ sparse InertialMeasurement Units (IMUs) on the human body and harness data-driven strategiesto model human poses. However, the reconstruction of motion based solely onsparse IMUs data is inherently fraught with ambiguity, a consequence ofnumerous identical IMU readings corresponding to different poses. In thispaper, we explore the spatial importance of multiple sensors, supervised bytext that describes specific actions. Specifically, uncertainty is introducedto derive weighted features for each IMU. We also design a HierarchicalTemporal Transformer (HTT) and apply contrastive learning to achieve precisetemporal and feature alignment of sensor data with textual semantics.Experimental results demonstrate our proposed approach achieves significantimprovements in multiple metrics compared to existing methods. Notably, withtextual supervision, our method not only differentiates between ambiguousactions such as sitting and standing but also produces more precise and naturalmotion.

Spatial-Related Sensors Matters: 3D Human Motion Reconstruction Assisted with Textual Semantics