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arXiv · 2603.00606

WristPP: A Wrist-Worn System for Hand Pose And Pressure Estimation

Ziheng Xi·Zihang Ao·Yitao Wang·Mingeze Gao·Wanmei Zhang·Jianjiang Feng·Jie Zhou
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Abstract

Accurate 3D hand pose and pressure sensing is essential for immersive human-computer interaction, yet simultaneously achieving both in mobile scenarios remains a significant challenge. We present WristPP, a camera-based wrist-worn system that estimates 3D hand pose and per-vertex pressure from a single wide-FOV RGB frame in real time. A Vision Transformer (ViT) backbone with joint-aligned tokens predicts Hand-VQVAE codebook indices for mesh recovery, while an extrinsics-conditioned branch jointly estimates per-vertex pressure. On a self-collected dataset of 133,000 frames (20 subjects; 48 on-plane and 28 mid-air gestures), WristPP attains a Mean Per-Joint Position Error (MPJPE) of 2.9 mm, Contact IoU of 0.712, Volumetric IoU of 0.618, and foreground pressure MAE of 10.4 g. Across three user studies, WristPP delivers touchpad-level efficiency in mid-air pointing and robust multi-finger pressure control on an uninstrumented desktop. In a real-world large-display Whac-A-Mole task, WristPP also enables higher success ratio and lower arm fatigue than head-mounted camera-based baselines. These results position WristPP as an effective, mobile solution for versatile pose- and pressure-based interaction. Website: https://zhenqis123.github.io/WristPP/.

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Ziheng Xi, Zihang Ao, Yitao Wang, Mingeze Gao, Wanmei Zhang, Jianjiang Feng, Jie Zhou. 2026-02-28. WristPP: A Wrist-Worn System for Hand Pose And Pressure Estimation. https://arxiv.org/abs/2603.00606

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