Reconstruction of Manipulated Garment with Guided Deformation Prior
This work addresses shape recovery for manipulated garments, which is a domain-specific problem in computer vision and graphics, with incremental improvements over existing garment modeling approaches.
The paper tackles the problem of reconstructing 3D shapes of garments when they are manipulated (e.g., folded) rather than worn, which involves a larger range of shapes, by extending an implicit sewing patterns model with a diffusion-based deformation prior and mapping incomplete 3D point clouds to UV space for recovery; it demonstrates superior reconstruction accuracy compared to previous methods, especially for large non-rigid deformations.
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.