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A COMPUTATIONAL APPROACH TO GENERATE DESIGN WITH SPECIFIC STYLE

Published online by Cambridge University Press:  27 July 2021

Da Wang ,
Jiaqi Li ,
Zhen Ge  and
Ji Han
Da Wang*
Affiliation:
University of Liverpool
Jiaqi Li
Affiliation:
University of Liverpool
Zhen Ge
Affiliation:
University of Dundee University of Technology Sydney
Ji Han
Affiliation:
University of Liverpool
*
Wang, Da, University of Liverpool, Industrial Design, United Kingdom, [email protected]

Abstract

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Creativity is crucial in design. In recent years, growing computational methods are applied to improve the creativity of design. This paper aims to explore an approach to generate creative design images with specific feature or design style. A Generative Adversarial Network model is applied in the approach to learn the specific design style. The target products will be projected into the latent space of model to transfer their styles and generate images. The generated images combine the features of the specific design style and the features of the target product. In the experiment, the approach using the generated images to inspire the human designer to generate the creative design in according styles. According to the primary verification by participants, the generated images can bring novelty and surprise to participants, which gain the positive impact on human creativity.

Keywords

Artificial intelligenceCreativityComputational design methods
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 21 - 30
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), 2021. Published by Cambridge University Press

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