Learning hatching for pen-and-ink illustration of surfaces
University of Toronto · Stanford University · +3 more institutions
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…
Citation impact
- FWCI
- 88.16
- Percentile
- 100%
- References
- 52
Authors
4Topics & keywords
- Hatching
- Artificial intelligence
- Computer science
- Cluster analysis
- Line drawings
- Object (grammar)
- Computer vision
- Pixel