用GAN融合文字、图像和风格生成高质量多模态图片
Generating Multimodal Images with GAN: Integrating Text, Image, and Style
- 设计文本编码器与风格融合模块，统一处理三类输入
- 在多个数据集上生成图像清晰度与风格一致性显著提升
- 适合需要精准控制图像内容与风格的研究者使用
在计算机视觉领域，多模态图像生成已成为研究热点，尤其是如何整合文本、图像与风格信息。本文提出一种基于生成对抗网络（GAN）的多模态图像生成方法，能够有效结合文本描述、参考图像和风格信息，生成满足多模态需求的图像。该方法包含文本编码器、图像特征提取器和风格融合模块，确保生成图像在视觉内容和风格一致性方面保持高质量。同时引入对抗损失、图文一致性损失和风格匹配损失等多种损失函数，优化生成过程。实验结果表明，该方法在多个公开数据集上均生成了高清晰度且风格一致的图像，相比现有方法有显著性能提升。研究为多模态图像生成提供了新思路，并展现出广阔的应用前景。
原文摘要 · Abstract (English)
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.
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