Organizations: University of Science and Technology Beijing, China · Department of ECE, The Hong Kong University of Science and Technology, Hong Kong · University of Exeter · Sony China Research Laboratory, China
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.
Figures & tables
Fig. 1: Overall framework of StructFlow-HPR for pose-conditioned CSI generation and downstream HPR augmentation.
State
PCK5
PCK10
PCK20
PCK30
A1
A2
A3
Δ
A1
A2
A3
Δ
A1
A2
A3
Δ
A1
A2
A3
Δ
Squat
67.84
76.93
77.79
+0.86
85.67
94.42
95.45
+1.03
91.46
99.77
99.84
+0.07
91.83
100.00
100.00
+0.00
Move
76.61
85.78
86.35
+0.57
89.58
98.39
99.08
+0.69
91.73
99.98
99.95
-0.03
92.16
100.00
100.00
+0.00
Rise Hand1
90.39
99.22
99.43
+0.21
91.27
99.91
99.91
+0.00
92.14
100.00
100.00
+0.00
91.76
100.00
100.00
+0.00
Rise Hand2
67.18
76.31
77.22
+0.91
84.53
93.29
94.14
+0.85
90.62
99.31
99.31
+0.00
91.49
99.84
99.68
-0.16
Press Leg1
43.82
52.99
53.63
+0.64
79.06
88.03
88.45
+0.42
88.74
97.31
97.13
-0.18
88.93
97.70
97.82
+0.11
TABLE I: Performance comparison between MetaFi, MfDfHPR, and StructFlow-HPR under various PCK thresholds (Motion States).
Fig. 2: t-SNE visualization of structured CSI latent distributions for real and StructFlow-generated samples under representative motion states.
Fig. 3: The human pose coordinates are generated by the visual model and our proposed StructFlow-HPR model, respectively.
Department of Intelligent Semiconductors, Soongsil University, Republic of Korea · Department of AI Convergence Security, Soongsil University, Republic of Korea · School of AI Software, Soongsil University, Republic of Korea
College of Electronic Information and Optical Engineering, Nankai University · School of Computing Science, University of Glasgow · School of Natural and Computing Science, University of Aberdeen