Electromagnetic (EM) body models predict the impact of human presence and motions on the Radio-Frequency (RF) field originated from wireless devices nearby. Despite their accuracy, EM models are time-consuming methods which prevent their adoption in strict real-time computational imaging problems and Bayesian estimation, such as passive localization, RF tomography, and holography. Physics-informed Generative Neural Network (GNN) models have recently attracted a lot of attention thanks to their potential to reproduce a process by incorporating relevant physical laws and constraints. They can be used to simulate or reconstruct missing data or samples, reproducing EM propagation effects, approximated EM fields and learn a physics-informed data distribution, i.e., a Bayesian prior. The paper discusses two popular techniques, namely variational auto-encoders (VAEs) and generative adversarial networks (GANs), and their adaptations to incorporate relevant EM body diffraction concepts. Proposed EM-informed generative models are verified against classical EM tools driven by diffraction theory and validated on real data. Physics-informed generative machine learning represents a multidisciplinary research area weaving together physical/EM modelling, signal processing and artificial intelligence (AI): the paper explores emerging opportunities of GNN tools targeting real-time passive RF sensing in multiple-input multiple-output (MIMO) communication systems. Proposed generative tools are designed, implemented and verified on resource constrained wireless devices being members of a Wireless Local Area Network (WLAN).