  Accurate estimation of anthropometric body measurements from RGB images has
many potential applications in industrial design, online clothing, medical
diagnosis and ergonomics. Research on this topic is limited by the fact that
there exist only generated datasets which are based on fitting a 3D body mesh
to 3D body scans in the commercial CAESAR dataset. For 2D only silhouettes are
generated. To circumvent the data bottleneck, we introduce a new 3D scan
dataset of 2,675 female and 1,474 male scans. We also introduce a small dataset
of 200 RGB images and tape measured ground truth. With the help of the two new
datasets we propose a part-based shape model and a deep neural network for
estimating anthropometric measurements from 2D images. All data will be made
publicly available.
