期刊
ACM TRANSACTIONS ON GRAPHICS
卷 31, 期 1, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2077341.2077342
关键词
Algorithms; Learning surface hatching; data-driven hatching; hatching by example; illustrations by example; learning orientation fields
资金
- NSERC
- CIFAR
- CFI
- Ontario MRI
- KAUST Global Collaborative Research
This article presents an algorithm for learning hatching styles from line drawings. An artist draws a single hatching illustration of a 3D object. Her strokes are analyzed to extract the following per-pixel properties: hatching level (hatching, cross-hatching, or no strokes), stroke orientation, spacing, intensity, length, and thickness. A mapping is learned from input geometric, contextual, and shading features of the 3D object to these hatching properties, using classification, regression, and clustering techniques. Then, a new illustration can be generated in the artist's style, as follows. First, given a new view of a 3D object, the learned mapping is applied to synthesize target stroke properties for each pixel. A new illustration is then generated by synthesizing hatching strokes according to the target properties.
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