  We consider the task of 3D joints location and orientation prediction from a
monocular video with the skinned multi-person linear (SMPL) model. We first
infer 2D joints locations with an off-the-shelf pose estimation algorithm. We
use the SPIN algorithm and estimate initial predictions of body pose, shape and
camera parameters from a deep regression neural network. We then adhere to the
SMPLify algorithm which receives those initial parameters, and optimizes them
so that inferred 3D joints from the SMPL model would fit the 2D joints
locations. This algorithm involves a projection step of 3D joints to the 2D
image plane. The conventional approach is to follow weak perspective
assumptions which use ad-hoc focal length. Through experimentation on the 3D
Poses in the Wild (3DPW) dataset, we show that using full perspective
projection, with the correct camera center and an approximated focal length,
provides favorable results. Our algorithm has resulted in a winning entry for
the 3DPW Challenge, reaching first place in joints orientation accuracy.
