P2S-Pose: A Physics Informed RF Sensing Framework for Structure Aware 3D Human Pose Estimation

Human pose estimation (HPE) from radio-frequency (RF) signals is challenged by propagation distortions, inter frequency coupling, and the lack of synergy between physical layer sensing and structural reasoning. In this letter, we propose P2S-Pose, a physics informed RF sensing framework that unifies signal modeling and structure aware inference. A Physics Informed Sensing Enhancement (PISE) stream disentangles frequency coupling, models propagation delays, and calibrates channel responses, yielding stable priors for volumetric inference. Guided by these cues, a Structure Aware Voxel Reasoning (SAVR) stage reconstructs per-bone volumetric fields with part aware supervision, producing interpretable and geometrically consistent intermediates. Compact bone and joint tokens are then fused by a Hierarchical Graph Fusion (HGF) module that performs cross level interaction and topology aware graph reasoning to regress anatomically valid 3D poses. Extensive experiments demonstrate that P2S-Pose achieves high accuracy, strong robustness, and effective generalization across subjects and actions, advancing RF-based 3D human sensing toward human centric wireless perception.