WiFi-GEN: High-Resolution Indoor Imaging from WiFi Signals Using Generative AI
Jianyang Shi, Bowen Zhang, Amartansh Dubey, Ross Murch, Liwen Jing
Abstract
Indoor imaging is a critical task for robotics and internet-ofthings. WiFi as an omnipresent signal is a promising candidate for carrying out passive imaging and synchronizing the up-to-date information to all connected devices. This is the first research work to consider WiFi indoor imaging as a multi-modal image generation task that converts the measured WiFi power into a high-resolution indoor image. Our proposedWiFi-GEN network achieves a shape reconstruction accuracy that is 275% of that achieved by physical model-based inversion methods. Additionally, the Frechet Inception Distance score has been significantly reduced by 82%. To examine the effectiveness of models for this task, the first large-scale dataset is released containing 80,000 pairs of WiFi signal and imaging target. Our model absorbs challenges for the model-based methods including the nonlinearity, ill-posedness and non-certainty into massive parameters of our generative AI network. The network is also designed to best fit measured WiFi signals and the desired imaging output. Code: https://github.com/CNFightingSjy/WiFiGEN
BibTeX
@inproceedings{icassp2026_wifigenhighresol,
title = {WiFi-GEN: High-Resolution Indoor Imaging from WiFi Signals Using Generative AI},
author = {Jianyang Shi and Bowen Zhang and Amartansh Dubey and Ross Murch and Liwen Jing},
booktitle = {ICASSP 2026},
year = {2026}
}