WiFi Person Pose Estimation via Interpretable Convolutional Modular for Person Spatial Map

With the rise of smart home applications, WiFi-based posture estimation has become a research focus. Existing methods often emphasize end-to-end feature extraction while neglecting high-resolution intermediate spatial features. We observe that horizontal and vertical antennas capture electromagnetic wave distributions in different directions, while multiple subcarrier frequencies inherently enhance multipath resolution. By analyzing signal correlations among subcarriers, we achieve equivalence to virtual antennas, enabling the generation of high-resolution spatial correlation maps. Using a cascaded network with UNet, we extract human spatial transformation features and a steering vector to obtain a spatial spectrum, mapping keypoints with high precision. Tests in through-wall environments achieve an average mPCK of 0.90 at 20 pixels and 0.98 at 50 pixels, significantly improving detection accuracy.