Abstract
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. © 2012 ACM.
Original language | English (US) |
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Pages (from-to) | 1-17 |
Number of pages | 17 |
Journal | ACM Transactions on Graphics |
Volume | 31 |
Issue number | 1 |
DOIs | |
State | Published - Jan 1 2012 |
Externally published | Yes |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledgements: This project was funded by NSERC, CIFAR, CFI, the Ontario MRI, and KAUST Global Collaborative Research.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.