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.
ACM CHI Conference on Human Factors in Computing Systems