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

TL;DR

WristPP is a wrist-worn system that accurately estimates 3D hand pose and pressure in real time using a single RGB camera, enabling versatile and efficient human-computer interaction in mobile scenarios.

Contribution

The paper introduces WristPP, a novel wrist-worn device that jointly estimates 3D hand pose and pressure from a single RGB frame, advancing mobile interaction capabilities.

Findings

Achieves 2.9 mm MPJPE in hand pose estimation

Attains 0.712 Contact IoU and 0.618 Volumetric IoU

Enables touchpad-level mid-air interaction and pressure control

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/.