  Groundbreaking applications such as ChatGPT have heightened research interest
in generative artificial intelligence (GAI). Essentially, GAI excels not only
in content generation but also in signal processing, offering support for
wireless sensing. Hence, we introduce a novel GAI-assisted human flow detection
system (G-HFD). Rigorously, G-HFD first uses channel state information (CSI) to
estimate the velocity and acceleration of propagation path length change of the
human-induced reflection (HIR). Then, given the strong inference ability of the
diffusion model, we propose a unified weighted conditional diffusion model
(UW-CDM) to denoise the estimation results, enabling the detection of the
number of targets. Next, we use the CSI obtained by a uniform linear array with
wavelength spacing to estimate the HIR's time of flight and direction of
arrival (DoA). In this process, UW-CDM solves the problem of ambiguous DoA
spectrum, ensuring accurate DoA estimation. Finally, through clustering, G-HFD
determines the number of subflows and the number of targets in each subflow,
i.e., the subflow size. The evaluation based on practical downlink
communication signals shows G-HFD's accuracy of subflow size detection can
reach 91%. This validates its effectiveness and underscores the significant
potential of GAI in the context of wireless sensing.
