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Accurate estimation of anthropometric body measurements from RGB images hasmany potential applications in industrial design, online clothing, medicaldiagnosis and ergonomics. Research on this topic is limited by the fact thatthere exist only generated datasets which are based on fitting a 3D body meshto 3D body scans in the commercial CAESAR dataset. For 2D only silhouettes aregenerated. To circumvent the data bottleneck, we introduce a new 3D scandataset of 2,675 female and 1,474 male scans. We also introduce a small datasetof 200 RGB images and tape measured ground truth. With the help of the two newdatasets we propose a part-based shape model and a deep neural network forestimating anthropometric measurements from 2D images. All data will be madepublicly available.

Learning Anthropometry from Rendered Humans