RFGAN: RF-Based Human Synthesis

TL;DR

This paper introduces RFGAN, a novel model that generates detailed human images from RF signals, enabling fine-grained optical human synthesis based on radio frequency sensing.

Contribution

The work presents the first method to generate optical human images from RF signals using a cross-modal GAN with RF-Extractor and adaptive normalization.

Findings

RFGAN successfully generates human activity images from RF signals.

The model outperforms existing rough perception RF sensing methods.

Two new datasets were created for RF-based human activity and image synthesis.

Abstract

This paper demonstrates human synthesis based on the Radio Frequency (RF) signals, which leverages the fact that RF signals can record human movements with the signal reflections off the human body. Different from existing RF sensing works that can only perceive humans roughly, this paper aims to generate fine-grained optical human images by introducing a novel cross-modal RFGAN model. Specifically, we first build a radio system equipped with horizontal and vertical antenna arrays to transceive RF signals. Since the reflected RF signals are processed as obscure signal projection heatmaps on the horizontal and vertical planes, we design a RF-Extractor with RNN in RFGAN for RF heatmap encoding and combining to obtain the human activity information. Then we inject the information extracted by the RF-Extractor and RNN as the condition into GAN using the proposed RF-based adaptive normalizations. Finally, we train the whole model in an end-to-end manner. To evaluate our proposed model, we create two cross-modal datasets (RF-Walk & RF-Activity) that contain thousands of optical human activity frames and corresponding RF signals. Experimental results show that the RFGAN can generate target human activity frames using RF signals. To the best of our knowledge, this is the first work to generate optical images based on RF signals.