  Many approaches to draping individual garments on human body models are
realistic, fast, and yield outputs that are differentiable with respect to the
body shape on which they are draped. However, they are either unable to handle
multi-layered clothing, which is prevalent in everyday dress, or restricted to
bodies in T-pose. In this paper, we introduce a parametric garment
representation model that addresses these limitations. As in models used by
clothing designers, each garment consists of individual 2D panels. Their 2D
shape is defined by a Signed Distance Function and 3D shape by a 2D to 3D
mapping. The 2D parameterization enables easy detection of potential collisions
and the 3D parameterization handles complex shapes effectively. We show that
this combination is faster and yields higher quality reconstructions than
purely implicit surface representations, and makes the recovery of layered
garments from images possible thanks to its differentiability. Furthermore, it
supports rapid editing of garment shapes and texture by modifying individual 2D
panels.
