  Modeling the shape of garments has received much attention, but most existing
approaches assume the garments to be worn by someone, which constrains the
range of shapes they can assume. In this work, we address shape recovery when
garments are being manipulated instead of worn, which gives rise to an even
larger range of possible shapes. To this end, we leverage the implicit sewing
patterns (ISP) model for garment modeling and extend it by adding a
diffusion-based deformation prior to represent these shapes. To recover 3D
garment shapes from incomplete 3D point clouds acquired when the garment is
folded, we map the points to UV space, in which our priors are learned, to
produce partial UV maps, and then fit the priors to recover complete UV maps
and 2D to 3D mappings. Experimental results demonstrate the superior
reconstruction accuracy of our method compared to previous ones, especially
when dealing with large non-rigid deformations arising from the manipulations.
