Learning hatching for pen-and-ink illustration of surfaces

Evangelos Kalogerakis, Derek Nowrouzezahrai, Simon Breslav, Aaron Hertzmann

Research output: Contribution to journalArticlepeer-review

48 Scopus citations

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 languageEnglish (US)
Pages (from-to)1-17
Number of pages17
JournalACM Transactions on Graphics
Volume31
Issue number1
DOIs
StatePublished - Jan 1 2012
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: 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.

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