A computational approach to generate design with specific style

Da Wang (Lead / Corresponding author), Jiaqi Li, Zhen Ge, Ji Han

    Research output: Contribution to journalConference articlepeer-review

    2 Citations (Scopus)
    42 Downloads (Pure)


    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.

    Original languageEnglish
    Pages (from-to)21-30
    Number of pages10
    JournalProceedings of the Design Society
    Publication statusPublished - 27 Jul 2021
    Event23rd International Conference on Engineering Design, ICED 2021 - Gothenburg, Sweden
    Duration: 16 Aug 202120 Aug 2021


    • Artificial intelligence
    • Computational design methods
    • Creativity

    ASJC Scopus subject areas

    • Computer Graphics and Computer-Aided Design
    • Computer Science Applications
    • Software
    • Modelling and Simulation


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