RGBD

POP: Full Parametric model Estimation for Occluded People

In the last decades, we have witnessed advances in both hardware and associated algorithms resulting in unprecedented access to volumes of 2D and, more recently, 3D data capturing human movement. We are no longer satisfied with recovering human pose as an image-space 2D skeleton, but seek to obtain a full 3D human body representation. The main challenges in acquiring 3D human shape from such raw measurements are identifying which parts of the data relate to body measurements and recovering from partial observations, often arising out of severe occlusion.

© Università degli Studi di Roma "La Sapienza" - Piazzale Aldo Moro 5, 00185 Roma