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

Abstract:With the growing demand for privacy-preserving and occlusion-resilient human pose recognition (HPR), 5G channel state information (CSI) offers a promising contactless sensing modality by integrating communication and sensing capabilities. However, collecting large-scale synchronized CSI-pose pairs remains costly in practical 5G systems. To address this limitation, we propose StructFlow-HPR, a structured pose-conditioned flow matching framework for generative CSI augmentation. StructFlow-HPR learns a continuous latent transport process from Gaussian noise to real CSI representations under pose guidance, while preserving the receiver-frequency topology of CSI through a reconstruction-preserving autoencoder. A pose-conditioned Transformer is further designed to model the latent velocity field and generate pose-aligned CSI samples via ordinary differential equation sampling. Experiments on real-world 5G sensing data show that StructFlow-HPR can produce realistic CSI-pose pairs and improve downstream HPR performance under limited-data conditions.

Signal Processing (eess.SP); Artificial Intelligence (cs.AI)

arXiv:2609.29912 [eess.SP]

(or arXiv:2609.29912v1 [eess.SP] for this version)

https://doi.org/10.48550/arXiv.2609.29912 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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