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

ISP: Multi-Layered Garment Draping with Implicit Sewing Patterns