CMU’s DensePose From WiFi: An Affordable, Accessible and Secure Approach to Human Sensing

The recent and rapid development of powerful machine learning models for computer vision has boosted 2D and 3D human pose estimation performance from RGB cameras, LiDAR, and radar inputs. These approaches however can require expensive and power-hungry hardware and have raised privacy concerns regarding their deployment in non-public areas.

A Carnegie Mellon University research team addresses these issues in the new paper DensePose From WiFi, proposing WiFi-based DensePose, a neural network architecture that uses only WiFi signals for human dense pose estimation in scenarios with occlusion and multiple people. The researchers believe their work could have practical applications in monitoring the well-being of elderly people or identifying suspicious behaviours in the home.

DensePose was introduced in 2018 and aims to map human pixels in an RGB image to the 3D surface of the human body. Synced has previously covered additional research…