  We introduce PLIKS (Pseudo-Linear Inverse Kinematic Solver) for
reconstruction of a 3D mesh of the human body from a single 2D image. Current
techniques directly regress the shape, pose, and translation of a parametric
model from an input image through a non-linear mapping with minimal flexibility
to any external influences. We approach the task as a model-in-the-loop
optimization problem. PLIKS is built on a linearized formulation of the
parametric SMPL model. Using PLIKS, we can analytically reconstruct the human
model via 2D pixel-aligned vertices. This enables us with the flexibility to
use accurate camera calibration information when available. PLIKS offers an
easy way to introduce additional constraints such as shape and translation. We
present quantitative evaluations which confirm that PLIKS achieves more
accurate reconstruction with greater than 10% improvement compared to other
state-of-the-art methods with respect to the standard 3D human pose and shape
benchmarks while also obtaining a reconstruction error improvement of 12.9 mm
on the newer AGORA dataset.
