Through-Wall Human Mesh Recovery Using Radio Signals
Name
rf-avatar.pdf
Description
Accepted version
Size
7.06 MB
Format
Adobe PDF
Checksum (MD5)
8e80075ad447ea79c38ef3aafcc38b0b
Author(s)
Zhao, Mingmin
Liu, Yingcheng
Raghu, Aniruddh
Zhao, Hang
Li, Tianhong
Torralba, Antonio
Katabi, Dina
Date Issued
February 2020
Journal
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Zhao, Mingmin et al. "Through-Wall Human Mesh Recovery Using Radio Signals." 2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 2019, Seoul, Korea, Institute of Electrical and Electronics Engineers, February 2020 © 2019 IEEE
Version
Author's final manuscript
Abstract
This paper presents RF-Avatar, a neural network model that can estimate 3D meshes of the human body in the presence of occlusions, baggy clothes, and bad lighting conditions. We leverage that radio frequency (RF) signals in the WiFi range traverse clothes and occlusions and bounce off the human body. Our model parses such radio signals and recovers 3D body meshes. Our meshes are dynamic and smoothly track the movements of the corresponding people. Further, our model works both in single and multi-person scenarios. Inferring body meshes from radio signals is a highly under-constrained problem. Our model deals with this challenge using: 1) a combination of strong and weak supervision, 2) a multi-headed self-attention mechanism that attends differently to temporal information in the radio signal, and 3) an adversarially trained temporal discriminator that imposes a prior on the dynamics of human motion. Our results show that RF-Avatar accurately recovers dynamic 3D meshes in the presence of occlusions, baggy clothes, bad lighting conditions, and even through walls.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/iccv.2019.01021
