The Methods and Mechanisms of Preserving Content and Structure in Image Style Transfer

Abstract

As artificial intelligence (AI) technology continues to advance, image style transfer has gradually moved from research to practical applications, becoming widely integrated into fields such as art creation, advertising design, and virtual reality. However, existing research often struggles to maintain the fidelity of the original image's content and structure while pursuing stylistic effects. This contradiction has become a key bottleneck restricting the further development of image style transfer technology. With advances in image processing, a key challenge is preserving the original image's structure and content while achieving a stylized effect. This paper reviews and analyzes the design principles and underlying mechanisms of various AI models, including Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), and Transformer architectures, in addressing the challenge of content preservation. Through the integration of existing literature, the results indicate that there is currently no universal solution, and trade-offs and choices need to be made for specific scenarios. Based on the limitations of existing methods, this paper explores potential future advancements in the technology, aiming to offer valuable insights for further development.