  Video capture is the most extensively utilized human perception source due to
its intuitively understandable nature. A desired video capture often requires
multiple environmental conditions such as ample ambient-light, unobstructed
space, and proper camera angle. In contrast, wireless measurements are more
ubiquitous and have fewer environmental constraints. In this paper, we propose
CSI2Video, a novel cross-modal method that leverages only WiFi signals from
commercial devices and a source of human identity information to recover
fine-grained surveillance video in a real-time manner. Specifically, two
tailored deep neural networks are designed to conduct cross-modal mapping and
video generation tasks respectively. We make use of an auto-encoder-based
structure to extract pose features from WiFi frames. Afterward, both extracted
pose features and identity information are merged to generate synthetic
surveillance video. Our solution generates realistic surveillance videos
without any expensive wireless equipment and has ubiquitous, cheap, and
real-time characteristics.
