Device-free 3D human pose estimation using commodity WiFi Channel State Information (CSI) enables privacy-preserving and illumination-robust human sensing, but its deployment is limited by poor cross-environment generalization. Unlike images, CSI measurements do not have a spatially localized correspondence to body parts and are heavily affected by multipath propagation, causing models that regress absolute poses to entangle body structure with environment-specific location cues.
Within a single environment this coupling is benign: an end-to-end absolute-pose variant, RePos-D, already achieves state-of-the-art accuracy on Person-in-WiFi-3D (86.9 mm MPJPE, a 3.4% gain over the previous best WiFi method). Across environments, however, the same model overfits position and degrades sharply. We therefore propose RePos, a factorized framework that separates root-relative pose estimation from root localization. By preventing absolute-pose supervision from affecting the structure branch, RePos keeps its pose representation environment-stable: CSI features are grouped into body-part latent queries that skeleton-guided modeling refines into the pose, while a separate amplitude-based network estimates the root position through a differentiable spatial-decomposition module.
Under the strict MM-Fi cross-environment protocol, RePos achieves MPJPEs of 254.4–296.1 mm, a 10–21% reduction over existing WiFi-based methods. The improvement is consistent across activity protocols, leave-one-environment-out splits, and leakage-free few-shot transfer. Analysis of the learned features shows that relative-pose representations remain largely position-agnostic, while root localization retains environment dependence: structure transfers across rooms, absolute placement does not.
