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