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.

翻译：服装形状建模已受到广泛关注，但现有方法大多假设服装由人体穿着，这限制了其可能呈现的形状范围。本研究针对服装在被操控而非穿着状态下的形状恢复问题，该场景下形状的可能性范围更大。为此，我们利用隐式缝纫图案（ISP）模型进行服装建模，并通过添加基于扩散的变形先验对其进行扩展，以表征这些形状。为了从折叠服装获取的不完整三维点云中恢复三维服装形状，我们将点映射到先验学习的UV空间，生成部分UV图，然后拟合先验以恢复完整的UV图和二维到三维映射。实验结果表明，相较于先前方法，我们的方法在重建精度上具有显著优势，尤其当处理由操控引起的大幅非刚性变形时表现更为突出。