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dc.contributor.authorSinha, Saptarshi Neilen_US
dc.contributor.authorWeinmann, Michaelen_US
dc.contributor.editorBucciero, Albertoen_US
dc.contributor.editorFanini, Brunoen_US
dc.contributor.editorGraf, Holgeren_US
dc.contributor.editorPescarin, Sofiaen_US
dc.contributor.editorRizvic, Selmaen_US
dc.date.accessioned2023-09-02T07:44:28Z
dc.date.available2023-09-02T07:44:28Z
dc.date.issued2023
dc.identifier.isbn978-3-03868-217-2
dc.identifier.issn2312-6124
dc.identifier.urihttps://doi.org/10.2312/gch.20231159
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/gch20231159
dc.description.abstractIn cultural heritage, portrait paintings and busts are special genres of artworks which are used to show the appearance and expression of a human subject. The purpose of such artwork is to serve as remembrance of the person who is depicted in that portrait or bust. The bust can moreover serve as a 3D representation of a portrait painting. Therefore, it would be interesting to stylize a portrait painting based on a specific bust, i.e. the generation of a 2D image of a bust corresponding to the person depicted in the portrait image. In this paper, we analyze and discuss the stylization of portrait paintings and photographs of human faces with busts using a deep learning based style transfer approach. To capture the characteristics in the appearance of busts, we created a novel dataset of busts and used DualStyleGAN for the use cases of stylizing portrait paintings and stylizing human faces based on our novel bust style. Our experiments show the potential of this approach. Stylizing human faces as busts might not only be appealing to experts that might save time and effort for generating an initial stylization to refine later on, but also increase the engagement of novice users and exhibition visitors with cultural heritage.en_US
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Machine learning; Image manipulation; Computer graphics; Applied computing → Arts and humanities
dc.subjectComputing methodologies → Machine learning
dc.subjectImage manipulation
dc.subjectComputer graphics
dc.subjectApplied computing → Arts and humanities
dc.titlePortrait2Bust: DualStyleGAN-based Portrait Image Stylization Based on Bust Sculpture Imagesen_US
dc.description.seriesinformationEurographics Workshop on Graphics and Cultural Heritage
dc.description.sectionheadersAI and 3D Reconstruction II
dc.identifier.doi10.2312/gch.20231159
dc.identifier.pages67-73
dc.identifier.pages7 pages


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Attribution 4.0 International License
Except where otherwise noted, this item's license is described as Attribution 4.0 International License