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