基于交互式大型语言模型和扩散模型的跨文化时尚设计
Cross-Cultural Fashion Design via Interactive Large Language Models and Diffusion Models
AI总结：
本文提出整合大型语言模型与潜在扩散模型的新框架，通过语义优化和弱监督过滤模块，在增强的DeepFashion+数据集上微调，有效解决了跨文化时尚生成中的文化偏见与图文对齐问题，实现了最优的图像生成质量与文化多样性。
摘要：
时尚内容生成是人工智能与创意设计交叉领域的新兴方向，应用涵盖从虚拟试衣到文化多元设计原型制作。现有方法常面临文化偏见、可扩展性有限以及文本提示与生成视觉之间对齐不足的问题，尤其是在弱监督下。本研究提出一个整合大型语言模型（LLMs）与潜在扩散模型（LDMs）的新颖框架以应对这些挑战。该方法利用LLMs对文本提示进行语义优化，并引入弱监督过滤模块以有效利用带噪或弱标签数据。通过在融合全球时尚风格、经增强的DeepFashion+数据集上微调LDM，所提方法达到了最先进的性能。实验结果表明，该方法显著优于基线，取得了更低的Frechet Inception Distance（FID）和更高的Inception Scores（IS），同时人工评估证实了其生成文化多元且语义相关时尚内容的能力。这些结果凸显了LLM引导的扩散模型在推动可扩展且包容的AI驱动时尚创新方面的潜力。
英文摘要：
Fashion content generation is an emerging area at the intersection of artificial intelligence and creative design, with applications ranging from virtual try-on to culturally diverse design prototyping. Existing methods often struggle with cultural bias, limited scalability, and alignment between textual prompts and generated visuals, particularly under weak supervision. In this work, we propose a novel framework that integrates Large Language Models (LLMs) with Latent Diffusion Models (LDMs) to address these challenges. Our method leverages LLMs for semantic refinement of textual prompts and introduces a weak supervision filtering module to effectively utilize noisy or weakly labeled data. By fine-tuning the LDM on an enhanced DeepFashion+ dataset enriched with global fashion styles, the proposed approach achieves state-of-the-art performance. Experimental results demonstrate that our method significantly outperforms baselines, achieving lower Frechet Inception Distance (FID) and higher Inception Scores (IS), while human evaluations confirm its ability to generate culturally diverse and semantically relevant fashion content. These results highlight the potential of LLM-guided diffusion models in driving scalable and inclusive AI-driven fashion innovation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
