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

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 WristP2, 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 ViT (Vision Transformer) backbone with joint-aligned tokens predicts Hand–VQ–VAE 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), WristP2 attains MPJPE (Mean Per-Joint Position Error) of 2.9 mm, Contact $\operatorname{IoU}$ of 0.712, $\operatorname{Vol.IoU}$ of 0.618, and foreground pressure MAE of 10.4 g. Across three user studies, WristP2 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, WristP2 also enables higher success ratio and lower arm fatigue than head-mounted camera-based baselines. These results position WristP2 as an effective, mobile solution for versatile pose- and pressure-based interaction.