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Towards a design generation methodology

Published: 14 August 2017 Publication History
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  • Abstract

    Inspired by the recent advances of deep learning and computer graphics techniques in generating sophisticated artistic styles and architectural designs, we envision a general design generation methodology that uses various specification and generative methods and tools in an integral design cycle.

    References

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    Brown, K. Grammatical Design. IEEE Expert, 1997, pp. 27--32.
    [2]
    Datta, R., Li, J., Wang, J. Z., Studying aesthetics in photographic images using a computational approach, Springer Berlin Heidelberg, US 8755596 B2{P}, 2014.
    [3]
    Gatys, L.A., Ecker, A. S., Bethge, M., Image Style Transfer Using Convolutional Neural Networks, Proc. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 2016, pp. 2414--2423.
    [4]
    Li, Y-N. Zhang, K., and Li, D.J., Rule-Based Automatic Generation of Logo Designs, Leonardo, MIT Press, Vol.50, No.2, April 2017, 177--181.
    [5]
    Stiny, G. & Gips J., Shape Grammars and the Generative Specification of Painting and Sculpture. Information Processing '71, 1972, pp.1460--1465.
    [6]
    Trescak. T., General Shape Grammar Interpreter for Intelligent Designs Generations. Proc. Sixth International Conference on Computer Graphics, Imaging and Visualization, 2009, pp.235--240.
    [7]
    Xiong, L., Zhang, K., Generation of Miro's Surrealism, Proc. 9th Symp. on Visual Information Communication and Interaction (VINCI'2016), Dallas, USA, 24--26 September 2016, ACM Press, 130--137.
    [8]
    Zhang, J.J., Yu, J.H., Zhang, K., Zheng, X.S., Zhang, J.S., Computational Aesthetic Evaluation of Logos, ACM Transactions on Applied Perception, accepted to appear, 2017.
    [9]
    Zhang, J.J., Zhang, K., Yu, J.H., Computer-Aided Generation of Mandala Thangka Patterns, Proc. 10th Symp. on Visual Information Communication and Interaction (VINCI'2017), Bangkok, Thailand, 14--16 August 2017, ACM Press.
    [10]
    Zhang, K. and Yu, J.H., Generation of Kandinsky Art, Leonardo, Vol. 49, No. 1, February 2016, MIT Press, 48--54.
    [11]
    Zhou, Y., Tan, Y., Li, G., Computational Aesthetic Measurement of Photographs Based on Multi-features with Saliency, Intelligent Computing Theory, 2014, 357-36.

    Cited By

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    • (2018)Computational aesthetics and applicationsVisual Computing for Industry, Biomedicine, and Art10.1186/s42492-018-0006-11:1Online publication date: 5-Sep-2018

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    Published In

    cover image ACM Other conferences
    VINCI '17: Proceedings of the 10th International Symposium on Visual Information Communication and Interaction
    August 2017
    158 pages
    ISBN:9781450352925
    DOI:10.1145/3105971
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

    Sponsors

    • KMUTT: King Mongkut's University of Technology Thonburi

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 14 August 2017

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    Author Tags

    1. design
    2. generation
    3. generative systems
    4. machine learning
    5. shape grammar

    Qualifiers

    • Poster

    Funding Sources

    • National High-tech R&D Program
    • National NSFC project

    Conference

    VINCI '17
    Sponsor:
    • KMUTT

    Acceptance Rates

    VINCI '17 Paper Acceptance Rate 12 of 27 submissions, 44%;
    Overall Acceptance Rate 71 of 193 submissions, 37%

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    • (2018)Computational aesthetics and applicationsVisual Computing for Industry, Biomedicine, and Art10.1186/s42492-018-0006-11:1Online publication date: 5-Sep-2018

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