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dc.contributor.authorZsolnai-Fehér, Károly
dc.contributor.authorWonka, Peter
dc.contributor.authorWimmer, Michael
dc.date.accessioned2018-12-23T08:06:03Z
dc.date.available2018-12-23T08:06:03Z
dc.date.issued2018-07-31
dc.identifier.citationZsolnai-Fehér K, Wonka P, Wimmer M (2018) Gaussian material synthesis. ACM Transactions on Graphics 37: 1–14. Available: http://dx.doi.org/10.1145/3197517.3201307.
dc.identifier.issn0730-0301
dc.identifier.doi10.1145/3197517.3201307
dc.identifier.urihttp://hdl.handle.net/10754/630344
dc.description.abstractWe present a learning-based system for rapid mass-scale material synthesis that is useful for novice and expert users alike. The user preferences are learned via Gaussian Process Regression and can be easily sampled for new recommendations. Typically, each recommendation takes 40-60 seconds to render with global illumination, which makes this process impracticable for real-world workflows. Our neural network eliminates this bottleneck by providing high-quality image predictions in real time, after which it is possible to pick the desired materials from a gallery and assign them to a scene in an intuitive manner.Workflow timings against Disney's
dc.description.sponsorshipWe would like to thank Robin Marin for the material test scene and Vlad Miller for his help with geometry modeling, Felícia Zsolnai–Fehér for improving the design of many figures, Hiroyuki Sakai, Christian Freude, Johannes Unterguggenberger, Pranav Shyam and Minh Dang for their useful comments, and Silvana Podaras for her help with a previous version of this work.We also thank NVIDIA for providing the GPU used to train our neural networks. This work was partially funded by Austrian Science Fund (FWF), project number P27974. Scene and geometry credits: Gold Bars – JohnsonMartin, Christmas Ornaments – oenvoyage, Banana – sgamusse, Bowl – metalix, Grapes – PickleJones, Glass Fruits – BobReed64, Ice cream – b2przemo, Vases – Technausea, Break Time – Jay–Artist, Wrecking Ball – floydkids, Italian Still Life – aXel, Microplanet – marekv, Microplanet vegetation – macio.
dc.publisherAssociation for Computing Machinery (ACM)
dc.relation.urlhttps://dl.acm.org/citation.cfm?doid=3197517.3201307
dc.rights© ACM, 2018. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Graphics, {37, 4, (2018-07-31)} http://doi.acm.org/10.1145/3197517.3201307
dc.subjectGaussian process regression
dc.subjectlatent variables
dc.subjectneural networks
dc.subjectneural rendering
dc.subjectphotorealistic rendering
dc.titleGaussian material synthesis
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.contributor.departmentVisual Computing Center (VCC)
dc.identifier.journalACM Transactions on Graphics
dc.eprint.versionPost-print
dc.contributor.institutionTU Wien, Vienna, Austria
dc.identifier.arxivid1804.08369
kaust.personWonka, Peter
dc.versionv1
refterms.dateFOA2018-12-23T08:25:42Z
dc.date.published-online2018-07-31
dc.date.published-print2018-07-30
dc.date.posted2018-04-23


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