  While methods that regress 3D human meshes from images have progressed
rapidly, the estimated body shapes often do not capture the true human shape.
This is problematic since, for many applications, accurate body shape is as
important as pose. The key reason that body shape accuracy lags pose accuracy
is the lack of data. While humans can label 2D joints, and these constrain 3D
pose, it is not so easy to "label" 3D body shape. Since paired data with images
and 3D body shape are rare, we exploit two sources of information: (1) we
collect internet images of diverse "fashion" models together with a small set
of anthropometric measurements; (2) we collect linguistic shape attributes for
a wide range of 3D body meshes and the model images. Taken together, these
datasets provide sufficient constraints to infer dense 3D shape. We exploit the
anthropometric measurements and linguistic shape attributes in several novel
ways to train a neural network, called SHAPY, that regresses 3D human pose and
shape from an RGB image. We evaluate SHAPY on public benchmarks, but note that
they either lack significant body shape variation, ground-truth shape, or
clothing variation. Thus, we collect a new dataset for evaluating 3D human
shape estimation, called HBW, containing photos of "Human Bodies in the Wild"
for which we have ground-truth 3D body scans. On this new benchmark, SHAPY
significantly outperforms state-of-the-art methods on the task of 3D body shape
estimation. This is the first demonstration that 3D body shape regression from
images can be trained from easy-to-obtain anthropometric measurements and
linguistic shape attributes. Our model and data are available at:
shapy.is.tue.mpg.de
