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SHAPE GENERATION SYSTEM FOR OPTIMIZING AESTHETIC INTEREST ASSOCIATED WITH NOVELTY AND COMPLEXITY

Published online by Cambridge University Press:  19 June 2023

Shimon Honda  and
Hideyoshi Yanagisawa
Shimon Honda*
Affiliation:
The University of Tokyo
Hideyoshi Yanagisawa
Affiliation:
The University of Tokyo
*
Honda, Shimon, The University of Tokyo, Japan, [email protected]

Abstract

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Design aesthetics are one of the most important factors affecting the attractiveness of industrial products. Psychological theory suggests that a moderate level of novelty and complexity yields pleasant feelings in users. A design that is initially surprising to consumers and acceptable over time requires aesthetic interest associated with its novelty and complexity. In this study, we formulated the perceived novelty and complexity of a closed contour shape. Based on this formulation, we developed “Hybrid-GAN,” which is a shape-generation system capable of generating a variety of shapes of arbitrary novelty and complexity. In a series of experiment, we obtained subjective evaluations of novelty and complexity, as well as beauty and interest, for the generated shape samples. The results indicated that our novelty and complexity formulations had significant positive correlations with subjective evaluations. The sum of the formulated novelty and complexity also had a significant positive correlation with interest. The results of this study are expected to be used to support the design of attractive shapes by providing feedback to designers regarding the degrees of novelty and complexity that users find most pleasant.

Keywords

Design for X (DfX)Design engineeringComplexityComputational design methods
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 3155 - 3164
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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