Benchmarking Non-Photorealistic Rendering of Portraits

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dc.contributor.authorRosin, Paul L.en_US
dc.contributor.authorMould, Daviden_US
dc.contributor.authorBerger, Itamaren_US
dc.contributor.authorCollomosse, Johnen_US
dc.contributor.authorLai, Yu-Kunen_US
dc.contributor.authorLi, Chuanen_US
dc.contributor.authorLi, Huaen_US
dc.contributor.authorShamir, Arielen_US
dc.contributor.authorWand, Michaelen_US
dc.contributor.authorWang, Tinghuaien_US
dc.contributor.authorWinnem, Holgeren_US
dc.contributor.editorHolger Winnemoeller and Lyn Bartramen_US
dc.date.accessioned2017-10-18T08:42:48Z
dc.date.available2017-10-18T08:42:48Z
dc.date.issued2017
dc.description.abstractWe present a set of images for helping NPR practitioners evaluate their image-based portrait stylisation algorithms. Using a standard set both facilitates comparisons with other methods and helps ensure that presented results are representative. We give two levels of di culty, each consisting of 20 images selected systematically so as to provide good coverage of several possible portrait characteristics. We applied three existing portraitspeci c stylisation algorithms, two generalpurpose stylisation algorithms, and one general learn ing based stylisation algorithm to the rst level of the benchmark, corresponding to the type of constrained images that have o ften been used in portraitspeci c work. We found that the existing methods are generally e ective on this new image set, demon strating that level one of the benchmark is tractable; challenges remain at level two. Results revealed several advantages conferred by portraitspeci c algorithms over generalpurpose algorithms: portraitspeci c algorithms can use domainspeci c information to preserve key details such as eyes and to eliminate extraneous details, and they have more scope for semantically meaningful abstraction due to the underlying face model. Finally, we pro vide some thoughts on systematically extending the benchmark to higher levels of di fficulty.en_US
dc.description.sectionheadersImage BCC (benchmarking, coloring, and clustering)
dc.description.seriesinformationNon-Photorealistic Animation and Rendering
dc.identifier.doi10.1145/3092919.3092921
dc.identifier.isbn978-1-4503-5081-5
dc.identifier.issn-
dc.identifier.urihttps://doi.org/10.1145/3092919.3092921
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/npar2017a11
dc.publisherAssociation for Computing Machinery, Inc (ACM)en_US
dc.subjectComputing methodologies
dc.subjectNon photorealistic rendering
dc.subjectImage processing
dc.subjectevaluation
dc.subjectnon
dc.subjectphotorealistic rendering
dc.subjectimage stylisation
dc.subjectportraits
dc.titleBenchmarking Non-Photorealistic Rendering of Portraitsen_US
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