  This paper addresses the problem of 3D human body shape and pose estimation
from RGB images. Some recent approaches to this task predict probability
distributions over human body model parameters conditioned on the input images.
This is motivated by the ill-posed nature of the problem wherein multiple 3D
reconstructions may match the image evidence, particularly when some parts of
the body are locally occluded. However, body shape parameters in widely-used
body models (e.g. SMPL) control global deformations over the whole body
surface. Distributions over these global shape parameters are unable to
meaningfully capture uncertainty in shape estimates associated with
locally-occluded body parts. In contrast, we present a method that (i) predicts
distributions over local body shape in the form of semantic body measurements
and (ii) uses a linear mapping to transform a local distribution over body
measurements to a global distribution over SMPL shape parameters. We show that
our method outperforms the current state-of-the-art in terms of
identity-dependent body shape estimation accuracy on the SSP-3D dataset, and a
private dataset of tape-measured humans, by probabilistically-combining local
body measurement distributions predicted from multiple images of a subject.
