[CHI'26] 3DRing: Enabling Low-Cost 3D Hand Position Tracking by Fusing Inertial and Low-Framerate Optical Sensing

Zhuojun Li, Lubin Wang, Chun Yu, Chang Liu, Mingyuan Du, Weinan Shi*, Yuanchun Shi

Posted by CaveSpider on April 13, 2026

Paper Abstract

Current mobile hand tracking systems primarily rely on high-framerate (HFR) optical sensors to capture hand positions, resulting in high computational cost and limiting the applicability in end devices. We propose 3DRing, a 3D hand position tracking method that requires only low-framerate (LFR, <10 FPS) optical data and a single IMU ring. It consists of two stages: (1) a Deep Extended Kalman Filter module that predicts high-framerate hand positions from LFR optical measurements and a single IMU; (2) a Reinforcement Learning module that adaptively selects minimal keyframes for calibration, further reducing the average optical framerate. Using only 6.61 FPS optical data, 3DRing achieves an average real-time tracking error of 1.75 cm and an interaction efficiency of 86.0% in a 3D target selection task, compared to the 67 FPS hand tracking system of Meta Quest Pro, demonstrating a strong potential to reduce the reliance on optical data in mobile hand tracking tasks.

[Paper] [DOI]

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