TailorMe: Self-Supervised Learning of an Anatomically Constrained
  Volumetric Human Shape Model
Stephan Wenninger Fabian Kemper Ulrich Schwanecke Mario Botsch
Human shape spaces have been extensively studied, as they are a core element
of human shape and pose inference tasks. Classic methods for creating a human
shape model register a surface template mesh to a database of 3D scans and use
dimensionality reduction techniques, such as Principal Component Analysis, to
learn a compact representation. While these shape models enable global shape
modifications by correlating anthropometric measurements with the learned
subspace, they only provide limited localized shape control. We instead
register a volumetric anatomical template, consisting of skeleton bones and
soft tissue, to the surface scans of the CAESAR database. We further enlarge
our training data to the full Cartesian product of all skeletons and all soft
tissues using physically plausible volumetric deformation transfer. This data
is then used to learn an anatomically constrained volumetric human shape model
in a self-supervised fashion. The resulting TailorMe model enables shape
sampling, localized shape manipulation, and fast inference from given surface
scans.