Generating Multimodal Images with GAN: Integrating Text, Image, and Style

2025-01-04

AI Summary

To address the challenge of jointly modeling textual semantics, reference images, and stylistic features in multimodal image generation, this paper proposes a unified three-modal (text, reference image, style) GAN framework. The method integrates BERT- or CLIP-encoded text embeddings, CNN-extracted reference image features, and learned style representations within a single generator—marking the first such joint encoding scheme. It further introduces an adaptive style integration module and a dual-objective loss function that jointly enforces text–image alignment and style fidelity. Evaluated on CUB and MS-COCO, the approach achieves a 23% reduction in FID, a 19% improvement in text–image alignment accuracy, and significantly superior style preservation compared to baselines including StyleGAN2 and TediGAN. The framework enables high-fidelity image synthesis that is semantically accurate, visually sharp, and stylistically consistent.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• Generative Adversarial Networks
• Multimodal Image Synthesis
• Text-to-Image Generation

Innovation

• GAN-based Image Generation
• Text-Image-Style Integration
• Quality Enhancement Strategies