Deep Detail Enhancement for Any Garment

Eurographics 2021 (Honorable Mention Best Paper Award)

Abstract

Creating fine garment details requires significant efforts and huge computational resources. In contrast, a coarse shape may be easy to acquire in many scenarios (e.g., via low-resolution physically-based simulation, linear blend skinning driven by skeletal motion, portable scanners). In this paper, we show how to enhance, in a data-driven manner, rich yet plausible details starting from a coarse garment geometry. Once the parameterization of the garment is given, we formulate the task as a style transfer problem over the space of associated normal maps. In order to facilitate generalization across garment types and character motions, we introduce a patch-based formulation, that produces high-resolution details by matching a Gram matrix based style loss, to hallucinate geometric details (i.e., wrinkle density and shape). We extensively evaluate our method on a variety of production scenarios and show that our method is simple, light-weight, efficient, and generalizes across underlying garment types, sewing patterns, and body motion.

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BibTex

@inproceedings{zhang2021deep, title={Deep detail enhancement for any garment}, author={Zhang, Meng and Wang, Tuanfeng and Ceylan, Duygu and Mitra, Niloy J}, booktitle={Computer Graphics Forum}, volume={40}, number={2}, pages={399–411}, year={2021}, organization={Wiley Online Library} }