Please use this identifier to cite or link to this item: https://doi.org/10.1109/CVPR.2012.6247762
Title: Synthesizing oil painting surface geometry from a single photograph
Authors: Luo, W.
Lu, Z. 
Wang, X.
Xu, Y.-Q.
Ben-Ezra, M.
Tang, X.
Brown, M.S. 
Issue Date: 2012
Source: Luo, W.,Lu, Z.,Wang, X.,Xu, Y.-Q.,Ben-Ezra, M.,Tang, X.,Brown, M.S. (2012). Synthesizing oil painting surface geometry from a single photograph. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition : 885-892. ScholarBank@NUS Repository. https://doi.org/10.1109/CVPR.2012.6247762
Abstract: We present an approach to synthesize the subtle 3D relief and texture of oil painting brush strokes from a single photograph. This task is unique from traditional synthesize algorithms due to its mixed modality between the input and output; i.e., our goal is to synthesize surface normals given an intensity image input. To accomplish this task, we propose a framework that first applies intrinsic image decomposition to produce a pair of initial normal maps. These maps are combined into a conditional random field (CRF) optimization framework that incorporates additional information derived from a training set consisting of normals captured using photometric stereo on oil paintings with similar brush styles. Additional constraints are incorporated into the CRF framework to further ensures smoothness and preserve brush stroke edges. Our results show that this approach can produce compelling reliefs that are often indistinguishable from results captured using photometric stereo. © 2012 IEEE.
Source Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
URI: http://scholarbank.nus.edu.sg/handle/10635/40761
ISBN: 9781467312264
ISSN: 10636919
DOI: 10.1109/CVPR.2012.6247762
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