Scientists from Carnegie Mellon University have developed a cheap way to sense humans through walls by using two Wi-Fi routers to image a human's 3D shape and pose. The researchers outline in a new paper how they used a deep neural network called DensePose that maps Wi-Fi signals (phase and amplitude) to UV coordinates, which is when a 3D model's surface is projected to a 2D image for mapping a computer-generated image. DensePose was developed by researchers at Imperial College London, Facebook AI, and University College London. The Carnegie Mellon researchers' key achievement with DensePose, first reported by Vice, is...