  With the proliferation of Wi-Fi devices in the environment, our surroundings
are increasingly illuminated with low-level RF scatter. This scatter
illuminates objects in the environment much like radar or LIDAR. We show that a
novel unsupervised network, based on the PIX2PIX GAN architecture, can recover
and visually reconstruct scene information solely from Wi-Fi background energy;
in contrast to a significantly less accurate approach by Kefayati (et. all)
which requires careful object labeling to recover object location from a scene.
This is accomplished by learning a more robust mapping function between the
channel state information (CSI) from Wi-Fi packets and Video image sample
distributions.
