  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.
