2025/01/04 by Chaoyi Tan, Wenqing Zhang, Tan, Chaoyi +8
Arts and Humanities · Health Professions · #Computer Vision and Pattern Recognition (cs.CV) #Digital Storytelling and Education #FOS: Computer and information sciences #Subtitles and Audiovisual Media #Translation Studies and Practices
paper · pdf · doi:10.48550/arxiv.2501.02167
openalex publication_date 2025/01/04 · openalex created_date 2025/01/08 · openalex updated_date 2026/07/28
In the field of computer vision, multimodal image generation has become a research hotspot, especially the task of integrating text, image, and style. In this study, we propose a multimodal image generation method based on Generative Adversarial Networks (GAN), capable of effectively combining text descriptions, reference images, and style information to generate images that meet multimodal requirements. This method involves the design of a text encoder, an image feature extractor, and a style integration module, ensuring that the generated images maintain high quality in terms of visual content and style consistency. We also introduce multiple loss functions, including adversarial loss, text-image consistency loss, and style matching loss, to optimize the generation process. Experimental results show that our method produces images with high clarity and consistency across multiple public datasets, demonstrating significant performance improvements compared to existing methods. The outcomes of this study provide new insights into multimodal image generation and present broad application prospects.