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dc.contributor.authorChristen, Fabienneen_US
dc.contributor.authorKim, Byungsooen_US
dc.contributor.authorAzevedo, Vinicius C.en_US
dc.contributor.authorSolenthaler, Barbaraen_US
dc.contributor.editorWilkie, Alexander and Banterle, Francescoen_US
dc.date.accessioned2020-05-24T13:42:41Z
dc.date.available2020-05-24T13:42:41Z
dc.date.issued2020
dc.identifier.isbn978-3-03868-101-4
dc.identifier.issn1017-4656
dc.identifier.urihttps://doi.org/10.2312/egs.20201015
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/egs20201015
dc.description.abstractArtistically controlling fluid simulations requires a large amount of manual work by an artist. The recently presented transportbased neural style transfer approach simplifies workflows as it transfers the style of arbitrary input images onto 3D smoke simulations. However, the method only modifies the shape of the fluid but omits color information. In this work, we therefore extend the previous approach to obtain a complete pipeline for transferring shape and color information onto 2D and 3D smoke simulations with neural networks. Our results demonstrate that our method successfully transfers colored style features consistently in space and time to smoke data for different input textures.en_US
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/]
dc.subjectComputing methodologies
dc.subjectPhysical simulation
dc.subjectImage processing
dc.subjectNeural networks
dc.titleNeural Smoke Stylization with Color Transferen_US
dc.description.seriesinformationEurographics 2020 - Short Papers
dc.description.sectionheadersModelling - Appearance
dc.identifier.doi10.2312/egs.20201015
dc.identifier.pages49-52


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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