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Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

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EgoPoseVR: Spatiotemporal Multi-Modal Reasoning for Egocentric Full-Body Pose in Virtual Reality.

Haojie Cheng, Shaun Jing Heng Ong, Shaoyu Cai

IEEE Transactions on Visualization and Computer Graphics

|April 3, 2026

Summary

EgoPoseVR enhances virtual reality (VR) full-body pose tracking using head-mounted display (HMD) motion and RGB-D data. This method achieves accurate, stable egocentric pose estimation without extra sensors.

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Area of Science:

Computer Vision
Virtual Reality
Human-Computer Interaction

Background:

Accurate full-body pose tracking is crucial for immersive virtual reality (VR) applications.
Current head-mounted camera approaches for egocentric pose estimation face challenges like temporal instability and inaccurate lower-body tracking in VR.

Purpose of the Study:

To develop an end-to-end framework, EgoPoseVR, for accurate egocentric full-body pose estimation in VR.
To address limitations of existing methods by integrating headset motion cues with RGB-D data.

Main Methods:

EgoPoseVR utilizes a dual-modality fusion pipeline combining headset motion cues and egocentric RGB-D observations.
A spatiotemporal encoder and cross-attention mechanism fuse frame- and joint-level representations.
A kinematic optimization module incorporates head-mounted display (HMD) signals for enhanced accuracy and stability.

Main Results:

EgoPoseVR demonstrated superior performance compared to state-of-the-art egocentric pose estimation models.
A large-scale synthetic dataset with over 1.8 million frames was introduced for training and evaluation.
User studies indicated significantly higher subjective ratings for accuracy, stability, embodiment, and future use intention.

Conclusions:

EgoPoseVR provides a robust solution for accurate full-body pose tracking in VR.
The framework enables practical VR embodiment without requiring additional body-worn sensors or room-scale tracking systems.