  Overcoming the visual barrier and developing "see-through vision" has been
one of mankind's long-standing dreams. Unlike visible light, Radio Frequency
(RF) signals penetrate opaque obstructions and reflect highly off humans. This
paper establishes a deep-learning model that can be trained to reconstruct
continuous video of a 15-point human skeleton even through visual occlusion.
The training process adopts a student/teacher learning procedure inspired by
the Feynman learning technique, in which video frames and RF data are first
collected simultaneously using a co-located setup containing an optical camera
and an RF antenna array transceiver. Next, the video frames are processed with
a computer-vision-based gait analysis "teacher" module to generate ground-truth
human skeletons for each frame. Then, the same type of skeleton is predicted
from corresponding RF data using a "student" deep-learning model consisting of
a Residual Convolutional Neural Network (CNN), Region Proposal Network (RPN),
and Recurrent Neural Network with Long-Short Term Memory (LSTM) that 1)
extracts spatial features from RF images, 2) detects all people present in a
scene, and 3) aggregates information over many time-steps, respectively. The
model is shown to both accurately and completely predict the pose of humans
behind visual obstruction solely using RF signals. Primary academic
contributions include the novel many-to-many imaging methodology, unique
integration of RPN and LSTM networks, and original training pipeline.
