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.

Camera-based person re-identification (ReID) systems have been widely applied in the field of public security. However, cameras often lack the perception of 3D morphological information of human and are susceptible to various limitations, such as inadequate illumination, complex background, and personal privacy. In this paper, we propose a LiDAR-based ReID framework, ReID3D, that utilizes pre-training strategy to retrieve features of 3D body shape and introduces Graph-based Complementary Enhancement Encoder for extracting comprehensive features. Due to the lack of LiDAR datasets, we build LReID, the first LiDAR-based person ReID dataset, which is collected in several outdoor scenes with variations in natural conditions. Additionally, we introduce LReID-sync, a simulated pedestrian dataset designed for pre-training encoders with tasks of point cloud completion and shape parameter learning. Extensive experiments on LReID show that ReID3D achieves exceptional performance with a rank-1 accuracy of 94.0, highlighting the significant potential of LiDAR in addressing person ReID tasks. To the best of our knowledge, we are the first to propose a solution for LiDAR-based ReID. The code and dataset are available at https://github.com/GWxuan/ReID3D.