4.7 Article

Learning Hatching for Pen-and-Ink Illustration of Surfaces

Journal

ACM TRANSACTIONS ON GRAPHICS
Volume 31, Issue 1, Pages -

Publisher

ASSOC COMPUTING MACHINERY
DOI: 10.1145/2077341.2077342

Keywords

Algorithms; Learning surface hatching; data-driven hatching; hatching by example; illustrations by example; learning orientation fields

Funding

  1. NSERC
  2. CIFAR
  3. CFI
  4. Ontario MRI
  5. KAUST Global Collaborative Research

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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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