EMHI: A Multimodal Egocentric Human Motion Dataset with HMD and Body-Worn IMUs

Understand

Egocentric human pose estimation (HPE) using wearable sensors is essential for VR/AR applications.

• Most methods rely solely on either egocentric-view images or sparse Inertial Measurement Unit (IMU) signals, leading to inaccuracies due to self-occlusion in images or the sparseness and drift of inertial sensors.
• Most importantly, the lack of real-world datasets containing both modalities is a major obstacle to progress in this field.
• To overcome the barrier, we propose EMHI, a multimodal \textbf{E}gocentric human \textbf{M}otion dataset with \textbf{H}ead-Mounted Display (HMD) and body-worn \textbf{I}MUs, with all data collected under the real VR product suite.