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DC poleHodnotaJazyk
dc.contributor.authorHarris, Mark Wesley
dc.contributor.authorSemwal, Sudhanshu
dc.contributor.editorSkala, Václav
dc.date.accessioned2021-08-31T07:09:19Z
dc.date.available2021-08-31T07:09:19Z
dc.date.issued2021
dc.identifier.citationWSCG 2021: full papers proceedings: 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 101-108.en
dc.identifier.isbn978-80-86943-34-3
dc.identifier.issn2464-4617
dc.identifier.issn2464–4625(CD/DVD)
dc.identifier.urihttp://hdl.handle.net/11025/45014
dc.format8 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.rights© Václav Skala - UNION Agencycs
dc.subjectgrafický kanálcs
dc.subjectvykreslovací technologiecs
dc.subjectstrojové učenícs
dc.subjectzpracování obrazucs
dc.subjectgenerativní kontradiktorní sítěcs
dc.subjecttext na obrázekcs
dc.subjectsémantické zpracování datcs
dc.titleDeep Rendering Graphics Pipelineen
dc.typeconferenceObjecten
dc.typekonferenční příspěvekcs
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThe graphics rendering pipeline is key to generating realistic images, and is a vital process of computational design,modeling, games, and animation. Perhaps the largest limiting factor of rendering is time; the processing requiredfor each pixel inevitably slows down rendering and produces a bottleneck which limits the speed and potential ofthe rendering pipeline. We applied deep generative networks to the complex problem of rendering an animated 3Dscene. Novel datasets of annotated image blocks were used to train an existing attentional generative adversarialnetwork to output renders of a 3D environment. The annotated Caltech-UCSD Birds-200-2011 dataset served asa baseline for comparison of loss and image quality. While our work does not yet generate production qualityrenders, we show how our method of using existing machine learning architectures and novel text and imageprocessing has the potential to produce a functioning deep rendering framework.en
dc.subject.translatedgraphics pipelineen
dc.subject.translatedrendering technologiesen
dc.subject.translatedmachine learningen
dc.subject.translatedimage processingen
dc.subject.translatedgenerative adversarial networksen
dc.subject.translatedtext-to-imageen
dc.subject.translatedsemantic data processingen
dc.identifier.doihttps://doi.org/10.24132/CSRN.2021.3101.11
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:WSCG 2021: Full Papers Proceedings

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