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Example‐based Authoring of Procedural Modeling Programs with Structural and Continuous Variability.

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Title: Example‐based Authoring of Procedural Modeling Programs with Structural and Continuous Variability.
Authors: Ritchie, Daniel1, Jobalia, Sarah2, Thomas, Anna2
Source: Computer Graphics Forum. May2018, Vol. 37 Issue 2, p401-413. 13p. 6 Diagrams, 3 Charts, 1 Graph.
Subjects: Computer graphics, Computer assisted instruction authoring software, Probabilistic generative models, Hierarchical clustering (Cluster analysis), Combinatorics
Abstract: Abstract: Procedural models are a powerful tool for quickly creating a variety of computer graphics content. However, authoring them is challenging, requiring both programming and artistic expertise. In this paper, we present a method for learning procedural models from a small number of example objects. We focus on the modular design setting, where objects are constructed from a common library of parts. Our procedural representation is a probabilistic program that models both the discrete, hierarchical structure of the examples as well as the continuous variability in their spatial arrangements of parts. We develop an algorithm for learning such programs from examples, using combinatorial search over program structures and variational inference to estimate continuous program parameters. We evaluate our method by demonstrating its ability to learn programs from examples of ornamental designs, spaceships, space stations, and castles. Experiments suggest that our learned programs can reliably generate a variety of new objects that are perceptually indistinguishable from hand‐crafted examples. [ABSTRACT FROM AUTHOR]
Copyright of Computer Graphics Forum is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Abstract: Procedural models are a powerful tool for quickly creating a variety of computer graphics content. However, authoring them is challenging, requiring both programming and artistic expertise. In this paper, we present a method for learning procedural models from a small number of example objects. We focus on the modular design setting, where objects are constructed from a common library of parts. Our procedural representation is a probabilistic program that models both the discrete, hierarchical structure of the examples as well as the continuous variability in their spatial arrangements of parts. We develop an algorithm for learning such programs from examples, using combinatorial search over program structures and variational inference to estimate continuous program parameters. We evaluate our method by demonstrating its ability to learn programs from examples of ornamental designs, spaceships, space stations, and castles. Experiments suggest that our learned programs can reliably generate a variety of new objects that are perceptually indistinguishable from hand‐crafted examples. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Computer Graphics Forum is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1111/cgf.13371
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        Text: English
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        PageCount: 13
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        Type: general
      – SubjectFull: Computer assisted instruction authoring software
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      – SubjectFull: Probabilistic generative models
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      – SubjectFull: Hierarchical clustering (Cluster analysis)
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      – SubjectFull: Combinatorics
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      – TitleFull: Example‐based Authoring of Procedural Modeling Programs with Structural and Continuous Variability.
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              Text: May2018
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