WiSPER: Pose-Supervised Predictive and Residual Flow Refinement For Multi-Person 3D Pose Estimation With WiFi CSI
Read the original on arXiv Computer Vision →WiSPER is a two‑stage framework for multi‑person 3D pose estimation using WiFi channel state information (CSI). The first stage, Pose‑Aware Masked Embedding Learning (PAMEL), couples masked latent prediction with pose‑set supervision to guide the encoder toward joint localization from partial observations. The second stage, Residual Flow refinement with Transformer (ReFT), generates pose candidates for a variable number of people and refines each candidate through a conditional flow guided by coarse coordinates and decoder features. Trained with paired CSI and pose annotations, WiSPER achieves a mean per‑joint position error of 63.72 mm on the PiW3D dataset, a 40.0 % improvement over WiFi‑JEPA and significant reductions for two‑ and three‑person scenarios.
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