Type
ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionComputer Science Program
Visual Computing Center (VCC)
Date
2018-07-31Preprint Posting Date
2018-04-23Online Publication Date
2018-07-31Print Publication Date
2018-07-30Permanent link to this record
http://hdl.handle.net/10754/630344
Metadata
Show full item recordAbstract
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'sCitation
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.Journal
ACM Transactions on GraphicsarXiv
1804.08369Additional Links
https://dl.acm.org/citation.cfm?doid=3197517.3201307ae974a485f413a2113503eed53cd6c53
10.1145/3197517.3201307