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Learnable Stroke Models for Example-based Portrait Painting

Wang, T, Collomosse, JP, Hunter, A and Greig, D Learnable Stroke Models for Example-based Portrait Painting In: British Machine Vision Conference (BMVC), 2013-09-01 - ?, Bristol.

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We present a novel algorithm for stylizing photographs into portrait paintings comprised of curved brush strokes. Rather than drawing upon a prescribed set of heuristics to place strokes, our system learns a flexible model of artistic style by analyzing training data from a human artist. Given a training pair — a source image and painting of that image—a non-parametric model of style is learned by observing the geometry and tone of brush strokes local to image features. A Markov Random Field (MRF) enforces spatial coherence of style parameters. Style models local to facial features are learned using a semantic segmentation of the input face image, driven by a combination of an Active Shape Model and Graph-cut. We evaluate style transfer between a variety of training and test images, demonstrating a wide gamut of learned brush and shading styles.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Wang, T
Collomosse, JP
Hunter, A
Greig, D
Contributors :
Additional Information : c 2013. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.
Depositing User : Symplectic Elements
Date Deposited : 03 Jun 2014 08:30
Last Modified : 31 Oct 2017 16:39

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