  This work presents a seminal approach for synthesizing images from WiFi
Channel State Information (CSI) in through-wall scenarios. Leveraging the
strengths of WiFi, such as cost-effectiveness, illumination invariance, and
wall-penetrating capabilities, our approach enables visual monitoring of indoor
environments beyond room boundaries and without the need for cameras. More
generally, it improves the interpretability of WiFi CSI by unlocking the option
to perform image-based downstream tasks, e.g., visual activity recognition. In
order to achieve this crossmodal translation from WiFi CSI to images, we rely
on a multimodal Variational Autoencoder (VAE) adapted to our problem specifics.
We extensively evaluate our proposed methodology through an ablation study on
architecture configuration and a quantitative/qualitative assessment of
reconstructed images. Our results demonstrate the viability of our method and
highlight its potential for practical applications.
