Seeing Through WiFi: Lightweight Human Pose Estimation with Dynamic Kernel Attention
WiFi-based human pose estimation (HPE) enables the detection and interpretation of human body positions and movements without the need for wearable devices while preserving individual privacy concerns. Implementing this solution requires enhancing model performance and maintaining efficiency, especially on resource-constrained devices. This paper introduces a novel framework, WiLHPE, for lightweight and efficient human pose estimation using WiFi CSI signals. Empowered by a camera-based model during training, WiLHPE processes raw WiFi signals directly to estimate human poses in the testing phase. It employs a novel neural network architecture to dynamically learn convolutional kernels and ...
| Authors | Toan D. Gian, Van-Dinh Nguyen, Vo Phi Son et al. |
|---|---|
| Primary category | cs.CV |
| Published | 3 July 2026 |
| Last updated at source | 3 July 2026 |
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