E. Kalogerakis, D. Nowrouzezahrai, S. Breslav, A. Hertzmann, Learning Hatching for Pen-and-Ink Illustration of Surfaces, To Appear in ACM Transactions on Graphics, 2012

Abstract:

This paper presents an algorithm for learning hatching styles from line drawings. An artist draws a single hatching illustration of a 3D object. Their 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. <p></p> <b><a href="https://graphics.stanford.edu/~kalo/papers/MLHatching/index.html">Project web page</a></b>

Bibtex: 

@article{knbh-lhpii-12,
Author    = {Evangelos Kalogerakis and Derek Nowrouzezahrai and Simon Breslav and Aaron Hertzmann},
Title     = {Learning {H}atching for {P}en-and-{I}nk {I}llustration of {S}urfaces},
Journal   = {ACM Transactions on Graphics},
Volume    = {31},
Number    = {1},
Year = {2012},
}