%0 Generic
%A Güler, RA
%A Kokkinos, I
%C Long Beach, CA, USA
%D 2019
%F discovery:10088677
%I Computer Vision Foundation / IEEE
%P 10884-10894
%T HoloPose: Holistic 3D Human Reconstruction In-The-Wild.
%U https://discovery.ucl.ac.uk/id/eprint/10088677/
%X We introduce HoloPose, a method for holistic monocular 3D human body reconstruction. We first introduce a  part-based model for 3D model parameter regression that  allows our method to operate in-the-wild, gracefully handling severe occlusions and large pose variation. We further  train a multi-task network comprising 2D, 3D and Dense  Pose estimation to drive the 3D reconstruction task. For  this we introduce an iterative refinement method that aligns  the model-based 3D estimates of 2D/3D joint positions and  DensePose with their image-based counterparts delivered  by CNNs, achieving both model-based, global consistency  and high spatial accuracy thanks to the bottom-up CNN  processing. We validate our contributions on challenging  benchmarks, showing that our method allows us to get both  accurate joint and 3D surface estimates, while operating  at more than 10fps in-the-wild. More information about  our approach, including videos and demos is available at  http://arielai.com/holopose.
%Z This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.