  Human shape estimation has become increasingly important both theoretically
and practically, for instance, in 3D mesh estimation, distance garment
production and computational forensics, to mention just a few examples. As a
further specialization, \emph{Human Body Dimensions Estimation} (HBDE) focuses
on estimating human body measurements like shoulder width or chest
circumference from images or 3D meshes usually using supervised learning
approaches. The main obstacle in this context is the data scarcity problem, as
collecting this ground truth requires expensive and difficult procedures. This
obstacle can be overcome by obtaining realistic human measurements from 3D
human meshes. However, a) there are no well established methods to calculate
HBDs from 3D meshes and b) there are no benchmarks to fairly compare results on
the HBDE task. Our contribution is twofold. On the one hand, we present a
method to calculate right and left arm length, shoulder width, and inseam
(crotch height) from 3D meshes with focus on potential medical, virtual try-on
and distance tailoring applications. On the other hand, we use four additional
body dimensions calculated using recently published methods to assemble a set
of eight body dimensions which we use as a supervision signal to our Neural
Anthropometer: a convolutional neural network capable of estimating these
dimensions. To assess the estimation, we train the Neural Anthropometer with
synthetic images of 3D meshes, from which we calculated the HBDs and observed
that the network's overall mean estimate error is $20.89$ mm (relative error of
2.84\%). The results we present are fully reproducible and establish a fair
baseline for research on the task of HBDE, therefore enabling the community
with a valuable method.
