arXiv AI By Haojin Li, Anbang Zhang, Wai Ho Mow, Chenyuan Feng, Chen Sun, Haijun Zhang

Structured Pose-Conditioned Flow Matching for Generative 5G CSI Augmentation

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The paper introduces StructFlow-HPR, a structured pose‑conditioned flow matching framework that generates realistic 5G channel state information (CSI) paired with human pose data. It learns a continuous latent transport process from Gaussian noise to real CSI, preserving the receiver‑frequency topology via a reconstruction‑preserving autoencoder. A pose‑conditioned Transformer models the latent velocity field, enabling pose‑aligned CSI generation through ordinary differential equation sampling, which improves human pose recognition performance when data are scarce.

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Sep 24

Structured Pose-Conditioned Flow Matching for Generative 5G CSI Augmentation

The paper introduces StructFlow-HPR, a structured pose‑conditioned flow matching framework that generates realistic 5G channel state information (CSI) paired with human pose data. It learns a continuous latent transport process from Gaussian noise to real CSI, preserving the receiver‑frequency topology via a reconstruction‑preserving autoencoder. A pose‑conditioned Transformer models the latent velocity field, enabling ordinary differential equation sampling to produce pose‑aligned CSI samples that improve human pose recognition performance in limited‑data scenarios.

arXiv Computer Vision
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WiSPER: Pose-Supervised Predictive and Residual Flow Refinement For Multi-Person 3D Pose Estimation With WiFi CSI

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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