# Through-Wall Imaging based on WiFi Channel State Information

arXiv: https://arxiv.org/abs/2401.17417 · HTML: https://mlsanity.com/paper/2401.17417/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Builds on (most cited first, 8 of 8)

- [Multimodal Generative Models for Scalable Weakly-Supervised Learning](https://mlsanity.com/paper/1802.05335/) (2018)
- [Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models](https://mlsanity.com/paper/1911.03393/) (2019)
- [Generalized Multimodal ELBO](https://mlsanity.com/paper/2105.02470/) (2021)
- [Brain decoding: toward real-time reconstruction of visual perception](https://mlsanity.com/paper/2310.19812/) (2023)
- [On a generalization of the Jensen-Shannon divergence and the JS-symmetrization of distances relying on abstract means](https://mlsanity.com/paper/1904.04017/) (2019)
- [RFMask: A Simple Baseline for Human Silhouette Segmentation with Radio Signals](https://mlsanity.com/paper/2201.10175/) (2022)
- [DensePose From WiFi](https://mlsanity.com/paper/2301.00250/) (2022)
- [RFGAN: RF-Based Human Synthesis](https://mlsanity.com/paper/2112.03727/) (2021)

## Built on by (most cited first, 4 of 4)

- [AM-FM: A Foundation Model for Ambient Intelligence Through WiFi](https://mlsanity.com/paper/2602.11200/) (2026)
- [High-resolution efficient image generation from WiFi CSI using a pretrained latent diffusion model](https://mlsanity.com/paper/2506.10605/) (2025)
- [Generative AI Meets Wireless Sensing: Towards Wireless Foundation Model](https://mlsanity.com/paper/2509.15258/) (2025)
- [Multi-Station WiFi CSI Sensing Framework Robust to Station-wise Feature Missingness and Limited Labeled Data](https://mlsanity.com/paper/2603.11858/) (2026)

## Abstract (arXiv)

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.