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A Data-Driven Creativity Measure for 3D Shapes

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Book cover Advances in Visual Computing (ISVC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12509))

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Abstract

There has been much interest in generating 3D shapes that are perceived to be “creative” and previous works develop tools that can be used to create shapes that may be considered “creative”. However, previous research either do not formally define what is a creative shape, or describe manually pre-defined methods or formulas to evaluate whether a shape is creative. In this paper, we develop a computational measure of 3D shape creativity by learning with raw data and without any pre-defined conception of creativity. We first collect various types of data on the human perception of 3D shape creativity. We then analyze the data to gain insights on what makes a shape creative, show results of our learned measure, and discuss some applications.

Manfred Lau acknowledges the Hong Kong Research Grants Council (General Research Fund numbers 11206319 and 11205420).

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Author information

Authors and Affiliations

  1. City University of Hong Kong, Hong Kong, China

    Manfred Lau

  2. Lancaster University, Lancaster, UK

    Luther Power

Authors
  1. Manfred Lau
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  2. Luther Power
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Corresponding author

Correspondence to Manfred Lau .

Editor information

Editors and Affiliations

  1. University of Nevada Reno, Reno, NV, USA

    George Bebis

  2. Stony Brook University, Stony Brook, NY, USA

    Zhaozheng Yin

  3. Drexel University, Philadelphia, PA, USA

    Edward Kim

  4. RWTH Aachen University, Aachen, Germany

    Jan Bender

  5. University of Edinburgh, Edinburgh, UK

    Kartic Subr

  6. IBM Research – Cambridge, Cambridge, MA, USA

    Bum Chul Kwon

  7. University of Waterloo, Waterloo, ON, Canada

    Jian Zhao

  8. Graz University of Technology, Graz, Austria

    Denis Kalkofen

  9. The Hong Kong Polytechnic University, Hong Kong, Hong Kong

    George Baciu

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Lau, M., Power, L. (2020). A Data-Driven Creativity Measure for 3D Shapes. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science(), vol 12509. Springer, Cham. https://doi.org/10.1007/978-3-030-64556-4_47

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  • DOI: https://doi.org/10.1007/978-3-030-64556-4_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64555-7

  • Online ISBN: 978-3-030-64556-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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