  In this paper we present CALVIS, a method to calculate $\textbf{C}$hest,
w$\textbf{A}$ist and pe$\textbf{LVIS}$ circumference from 3D human body meshes.
Our motivation is to use this data as ground truth for training convolutional
neural networks (CNN). Previous work had used the large scale CAESAR dataset or
determined these anthropometrical measurements $\textit{manually}$ from a
person or human 3D body meshes. Unfortunately, acquiring these data is a cost
and time consuming endeavor. In contrast, our method can be used on 3D meshes
automatically. We synthesize eight human body meshes and apply CALVIS to
calculate chest, waist and pelvis circumference. We evaluate the results
qualitatively and observe that the measurements can indeed be used to estimate
the shape of a person. We then asses the plausibility of our approach by
generating ground truth with CALVIS to train a small CNN. After having trained
the network with our data, we achieve competitive validation error.
Furthermore, we make the implementation of CALVIS publicly available to advance
the field.
