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Enhancing computer graphics through machine learning: a survey

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Abstract

Machine learning has experienced explosive growth in the last few decades, achieving sufficient maturity to provide effective tools for sundry scientific and engineering fields. Machine learning provides a firm theoretical foundation upon which to build techniques that leverage existing data to extract interesting information or to synthesize more data.

In this paper we survey the uses of machine learning methods and concepts in recent computer graphics techniques. Many graphics techniques are data-driven; however, few graphics papers explicitly leverage the machine learning literature to underpin, validate, and develop their proposed methods. This survey provides novel insights by casting many existing computer graphics techniques into a common learning framework. This not only illuminates how these techniques are related, but also reveals possible ways in which they may be improved. We also use our analysis to propose several directions for future work.

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  1. PDI/DreamWorks Animation, Glendale, CA, USA

    Jonathan Dinerstein

  2. Brigham Young University, Provo, UT, USA

    Parris K. Egbert & David Cline

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Dinerstein, J., Egbert, P. & Cline, D. Enhancing computer graphics through machine learning: a survey. Visual Comput 23, 25–43 (2007). https://doi.org/10.1007/s00371-006-0085-4

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