ZoeMatrope

Fig. 1. Our system opens up the possibility of rapid mass-scale material synthesis for novice and expert users alike. This method takes a set of user preferences as an input and recommends relevant new materials from the learned distributions. On the left, we populated a scene with metals and minerals, translucent, glittery and glassy materials, each of which was learned and synthesized via our proposed technique. The image on the right showcases rich material variations for more than a hundred synthesized materials and objects for the vegetation of the planet. The learning and recommendation steps take less than a minute.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 łprincipledž shader reveal that our system scales well with the number of sought materials,

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Gaussian material synthesis

Zsolnai-Fehér

Wonka

Wimmer

2018

ACM Trans. Graph.

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ZoeMatrope

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Gaussian material synthesis

Gaussian material synthesis

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