3D human pose and shape (HPS): Methods that re-
construct 3D human bodies from one or more RGB im-
ages can be split into two broad categories: (1) parametric
methods that predict parameters of a statistical 3D body
model, such as SCAPE [3], SMPL [37], SMPL-X [43],
Adam [26], GHUM [65], and (2) non-parametric methods
that predict a free-form representation of the human body
[24, 50, 59, 64]. Parametric approaches lack details w.r.t.
non-parametric ones, e.g., clothing or hair. However, para-
metric models disentangle the effects of identity and pose
on the overall shape. Therefore, their parameters provide
control for re-shaping and re-posing. Moreover, pose can
be factored out to bring meshes in a canonical pose; this is
important for evaluating estimates of an individual’s shape.
Finally, since topology is fixed, meshes can be compared
easily. For these reasons, we use a SMPL-X body model.
Accurate 3D Body Shape Regression using Metric and Semantic Attributes