RF-Pose3D is the first system that infers 3D human skeletons from RF signals.
It requires no sensors on the body, and works with multiple people and across walls and occlusions.
Further, it generates dynamic skeletons that follow the people as they move, walk or sit.
As such, RF-Pose3D provides a significant leap in RF-based sensing and enables new applications in gaming, healthcare, and smart homes.
RF-Pose3D is based on a novel convolutional neural network (CNN) architecture that performs high-dimensional convolutions by decomposing them into low-dimensional operations.
This property allows the network to efficiently condense the spatio-temporal information in RF signals.
The network first zooms in on the individuals in the scene, and crops the RF signals reflected off each person.
For each individual, it localizes and tracks their body parts -- head, shoulders, arms, wrists, hip, knees, and feet.
We have openings for postdocs and interns! If you are interested in working on exciting projects like RF-Pose, please send an email along with your resume to Prof. Dina Katabi at firstname.lastname@example.org.