A computational approach to generate design with specific style

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.