RePos：用于跨环境基于WiFi的3D人体姿态估计的相对到绝对输出分解
RePos: Relative-to-Absolute Pose Factorization for Cross-Environment WiFi-Based 3D Human Pose Estimation
- Faculty of Science and Technology, Keio University（庆应义塾大学理工学部）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究跨环境WiFi 3D人体姿态估计问题，提出RePos框架，将根相对姿态估计与根定位分离，防止绝对姿态监督影响结构分支，提升跨环境性能。
AI中文摘要：
使用商用WiFi信道状态信息（CSI）进行无设备3D人体姿态估计可实现隐私保护和光照鲁棒的人体感知，但跨环境泛化性差。本文提出RePos框架，分离根相对姿态估计与根定位，学习环境不变的姿态表示，在严格协议下性能提升，分析表明相对姿态表示与位置无关，根定位依赖环境。
英文摘要：
Device-free 3D human pose estimation from commodity WiFi Channel State Information (CSI) enables human sensing that preserves privacy and tolerates poor illumination, but its deployment is limited by poor generalization across environments. Unlike images, CSI measurements have no spatially localized correspondence to body parts and are heavily affected by multipath propagation. Consequently, models that regress absolute poses entangle body structure with location cues specific to each environment. Within a single environment this coupling is not problematic: RePos-D, a direct model that regresses the absolute pose, already achieves the best reported accuracy on Person-in-WiFi-3D, a 3.4% gain over the previous best WiFi method, DT-Pose. 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 shielding the structure branch from absolute position, RePos learns robust pose representations. Specifically, it groups CSI features into latent tokens organized by body part that a skeleton-guided module refines into the pose, while a separate network estimates the root position from CSI amplitude through a differentiable spatial decomposition. Under the strict MM-Fi cross-environment protocol, RePos reduces the mean per-joint position error (MPJPE) by 10-21% over existing WiFi methods. The improvement is consistent across activity protocols, holds when each environment is held out in turn, and survives few-shot transfer without data leakage. Further analysis shows that the relative pose predictions remain largely independent of position, whereas root localization remains dependent on the environment.
