Abstract. Recent capture technologies and methods allow not only to
retrieve 3D model sequence of moving people in clothing, but also to
separate and extract the underlying body geometry, motion component
and the clothing as a geometric layer. So far this clothing layer has only
been used as raw offsets for individual applications such as retargeting
a different body capture sequence with the clothing layer of another sequence, with limited scope, e.g. using identical or similar motions. The
structured, semantics and motion-correlated nature of the information
contained in this layer has yet to be fully understood and exploited. To
this purpose we propose a comprehensive analysis of the statistics of
this layer with a simple two-component model, based on PCA subspace
reduction of the layer information on one hand, and a generic parameter regression model using neural networks on the other hand, designed
to regress from any semantic parameter whose variation is observed in
a training set, to the layer parameterization space. We show that this
model not only allows to reproduce previous retargeting works, but generalizes the data generation capabilities to other semantic parameters
such as clothing variation and size, or physical material parameters with
synthetically generated training sequence, paving the way for many kinds
of capture data-driven creation and augmentation applications.