  Electromagnetic (EM) body models designed to predict Radio-Frequency (RF)
propagation are time-consuming methods which prevent their adoption in strict
real-time computational imaging problems, such as human body localization and
sensing. Physics-informed Generative Neural Network (GNN) models have been
recently proposed to reproduce EM effects, namely to simulate or reconstruct
missing data or samples by incorporating relevant EM principles and
constraints. The paper discusses a Variational Auto-Encoder (VAE) model which
is trained to reproduce the effects of human motions on the EM field and
incorporate EM body diffraction principles. Proposed physics-informed
generative neural network models are verified against both classical
diffraction-based EM tools and full-wave EM body simulations.
