[Paper Review] Generating Multimodal Images with GAN: Integrating Text, Image, and Style

Chaoyi Tan, Wenqing Zhang+4 more|arXiv (Cornell University)|Jan 4, 2025

Subtitles and Audiovisual Media4 citations

TL;DR

This paper introduces a GAN-based method to generate multimodal images by integrating text descriptions, reference images, and style information, with new loss terms to ensure content and style alignment.

ABSTRACT

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.

Motivation & Objective

• Motivate multimodal image generation that fuses textual, visual, and stylistic cues.
• Develop a GAN framework that can jointly utilize text descriptions, reference images, and style information.
• Ensure high-quality visuals with content fidelity and style consistency across outputs.
• Propose loss functions to optimize text-image consistency and style matching.

Proposed method

• Design a multimodal GAN architecture with a text encoder, an image feature extractor, and a style integration module.
• Introduce adversarial loss to drive realism in generated images.
• Incorporate text-image consistency loss to align generated visuals with textual inputs.
• Apply style matching loss to ensure stylistic coherence with the provided style cues.
• Evaluate the method on multiple public datasets to assess image quality and cross-modal consistency.

Experimental results

Research questions

• RQ1 Can a GAN-based framework effectively combine text descriptions, reference images, and style information to generate coherent multimodal images?
• RQ2 Do the proposed losses (adversarial, text-image consistency, style matching) improve fidelity and style alignment over baseline methods?
• RQ3 How does the method perform across different public datasets in terms of visual quality and multimodal consistency?

Key findings

• The method produces images with high clarity and consistency across multiple public datasets.
• The approach demonstrates significant performance improvements over existing methods according to the abstract.
• The results provide new insights into multimodal image generation and broad application prospects.