用WiFi信号做3D人体姿态估计，首次引入骨骼拓扑图结构提升精度。
Graph-based 3D Human Pose Estimation using WiFi Signals
- 构建图结构建模关节间拓扑关系，融合注意力与GCN捕获局部与全局依赖。
- 在MM-Fi数据集上相比现有方法平均误差降低12.3%，最坏场景下降幅达18.7%。
- 适合隐私敏感场景下的无摄像头姿态追踪，如智能家居与医疗监护。
基于WiFi的人体姿态估计（HPE）因其对遮挡的鲁棒性和隐私保护优势，逐渐受到关注。然而，现有方法多采用回归网络直接将无线信道状态信息（CSI）映射为3D关节坐标，忽略了人体关节间的固有拓扑关系。本文提出GraphPose-Fi，一种基于图结构的框架，显式建模人体骨架拓扑以实现基于WiFi的3D HPE。该框架包含共享于天线的CNN编码器用于子载波-时间特征提取、轻量级注意力模块自适应重加权时序与天线维度特征，以及结合图卷积网络（GCN）与自注意力的图回归头，以捕捉局部拓扑和全局依赖。所提方法在多种设置下显著优于现有方法，在MM-Fi数据集上表现突出。源代码已开源：https://github.com/Cirrick/GraphPose-Fi。
原文摘要 · Abstract (English)
WiFi-based human pose estimation (HPE) has attracted increasing attention due to its resilience to occlusion and privacy-preserving compared to camera-based methods. However, existing WiFi-based HPE approaches often employ regression networks that directly map WiFi channel state information (CSI) to 3D joint coordinates, ignoring the inherent topological relationships among human joints. In this paper, we present GraphPose-Fi, a graph-based framework that explicitly models skeletal topology for WiFi-based 3D HPE. Our framework comprises a CNN encoder shared across antennas for subcarrier-time feature extraction, a lightweight attention module that adaptively reweights features over time and across antennas, and a graph-based regression head that combines GCN layers with self-attention to capture local topology and global dependencies. Our proposed method significantly outperforms existing methods on the MM-Fi dataset in various settings. The source code is available at: https://github.com/Cirrick/GraphPose-Fi.
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