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.

使用 WiFi Channel State Information（CSI）进行图像合成，通过墙壁场景下的图像监测方法来改进 WiFi CSI 的可解释性并实现基于图像的下游任务，如视觉活动识别。