Product Image Generation Method Based on Morphological Optimization and Image Style Transfer - Aimin Zhou; Xinle Wang; Yujin Huang; Weitang Wang; Shutao Zhang; Jinyan Ouyang (Applied Sciences, 2025)
In order to improve the controllability and esthetics of product image generation, from the perspective of design, this study proposes a product image generation method based on morphological optimization, esthetic evaluation, and style transfer. Firstly, based on computational esthetics and principles of visual perception, an esthetic comprehensive evaluation model is constructed and used as the fitness function. The genetic algorithm is employed to build a product morphological optimization design system, obtaining product form schemes with higher esthetic quality. Then, an automobile front-end image dataset is constructed, and a generative adversarial network model is trained. Using the aforementioned product form scheme as the content image and selecting automobile front-end images from the market as the target style image, the content features and style features are extracted by the encoder and input into the generator to generate style-transferred images. The discriminator is utilized for judgment, and through iterative optimization, product image schemes that meet the target style are obtained. Experimental results demonstrate that the model generates product images with good effects, showcasing the feasibility of the method and providing robust technical support for intelligent product image design.