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    Gaussian material synthesis

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    Name:
    2018.SG.Karoly.GaussianMaterial.pdf
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    51.42Mb
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    Description:
    Accepted Manuscript
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    2018.SG.Karoly.GaussianMaterial.Additional.pdf
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    Type
    Article
    Authors
    Zsolnai-Fehér, Károly
    Wonka, Peter cc
    Wimmer, Michael
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Visual Computing Center (VCC)
    Date
    2018-07-31
    Preprint Posting Date
    2018-04-23
    Online Publication Date
    2018-07-31
    Print Publication Date
    2018-07-30
    Permanent link to this record
    http://hdl.handle.net/10754/630344
    
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    Abstract
    We 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
    Citation
    Zsolnai-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.
    Sponsors
    We 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.
    Publisher
    Association for Computing Machinery (ACM)
    Journal
    ACM Transactions on Graphics
    DOI
    10.1145/3197517.3201307
    arXiv
    1804.08369
    Additional Links
    https://dl.acm.org/citation.cfm?doid=3197517.3201307
    ae974a485f413a2113503eed53cd6c53
    10.1145/3197517.3201307
    Scopus Count
    Collections
    Articles; Computer Science Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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