Estimating 3D body shape: State-of-the-art methods
are effective for estimating 3D pose, but struggle with es-
timating body shape under clothing. There are several rea-
sons for this. First, 2D keypoints alone are not sufficient
to fully constrain 3D body shape. Second, shape priors ad-
dress the lack of constraints, but bias solutions towards “av-
erage” shapes [7,33,40,43]. Third, datasets with in-the-wild
images have noisy 3D bodies, recovered by fitting a model
to 2D keypoints [7, 43]. Fourth, datasets captured in labo-
ratory settings have a small number of subjects, who do not
represent the full spectrum of body shapes. Thus, there is
a scarcity of images with known, accurate, 3D body shape.
Existing methods deal with this in two ways.
Accurate 3D Body Shape Regression using Metric and Semantic Attributes