Semantic Stylization and Shading via Segmentation Atlas utilizing Deep Learning Approaches

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dc.contributor.authorSinha, Saptarshi Neilen_US
dc.contributor.authorKühn, Paul Juliusen_US
dc.contributor.authorRojtberg, Pavelen_US
dc.contributor.authorGraf, Holgeren_US
dc.contributor.authorKuijper, Arjanen_US
dc.contributor.authorWeinmann, Michaelen_US
dc.contributor.editorCaputo, Arielen_US
dc.contributor.editorGarro, Valeriaen_US
dc.contributor.editorGiachetti, Andreaen_US
dc.contributor.editorCastellani, Umbertoen_US
dc.contributor.editorDulecha, Tinsae Gebrechristosen_US
dc.date.accessioned2024-11-11T12:48:49Z
dc.date.available2024-11-11T12:48:49Z
dc.date.issued2024
dc.description.abstractWe present a novel hybrid approach for semantic stylization of surface materials of 3D models while preserving shading. Based on a hybrid approach that builds on directly applying style transfer on the object surface obtained by learning-based or traditional methods such as 3D scanners or structured light systems, thereby overcoming artifacts like halos, ghosting or lacking quality of the geometric representation produced by other 3D stylization methods. For this purpose, our methods involves (i) the initial generation of a segmentation map parameterized over the object surface inferred based on a deep-learning-based foundation model to guide the stylization and shading of different regions of the 3D model, and (ii) a subsequent 2D style transfer that allows the exchange or stylization of surface materials in high quality. By delivering high-quality semantic perceptive reconstructions in a shorter timeframe than current approaches using manual 3D segmentation and stylization, our approach holds significant potential for various application scenarios including creative design, architecture and cultural heritage.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationSmart Tools and Applications in Graphics - Eurographics Italian Chapter Conference
dc.identifier.doi10.2312/stag.20241352
dc.identifier.isbn978-3-03868-265-3
dc.identifier.issn2617-4855
dc.identifier.pages3 pages
dc.identifier.urihttps://doi.org/10.2312/stag.20241352
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/stag20241352
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 → Computer graphics; Reflectance modeling; Image manipulation; Scene understanding
dc.subjectComputing methodologies → Computer graphics
dc.subjectReflectance modeling
dc.subjectImage manipulation
dc.subjectScene understanding
dc.titleSemantic Stylization and Shading via Segmentation Atlas utilizing Deep Learning Approachesen_US
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Italian Chapter Conference 2024 - Smart Tools and Apps in Graphics