TY  - JOUR
VL  - 40
AV  - public
Y1  - 2021/05/01/
SP  - 399
EP  - 411
TI  - Deep Detail Enhancement for Any Garment
IS  - 2
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
UR  - http://dx.doi.org/10.1111/cgf.142642
N2  - 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. Project page: http://geometry.cs.ucl.ac.uk/projects/2021/DeepDetailEnhance/.
ID  - discovery10130167
A1  - Zhang, M
A1  - Wang, T
A1  - Ceylan, D
A1  - Mitra, NJ
JF  - Computer Graphics Forum
ER  -