基于LMM的Image-into-Prompt基准和数据集的细粒度定制化时尚设计
Fine-Grained Customized Fashion Design with Image-into-Prompt benchmark and dataset from LMM
AI总结：
针对时尚设计中文本不确定性导致的细粒度定制难题，提出结合LMM的BUG工作流，通过image-into-prompt自动生成并定制服装设计，并构建FashionEdit数据集验证其有效性。
摘要：
生成式AI推动了工业界复杂工作流的执行，其中大型多模态模型为服装业的时尚设计赋能。当前的生成式AI模型能轻易将头脑风暴转化为奇妙设计，但细粒度定制仍因终端用户缺乏专业背景知识而受制于文本不确定性。因此，我们提出结合LMM的Better Understanding Generation (BUG)工作流，通过image-into-prompt从对话中自动创建并细粒度定制服装设计。该框架释放了用户超越文字的创造潜力，且无需进一步人工介入即降低了服装设计/编辑门槛。为证明有效性，我们提出模拟真实服装设计工作流的新FashionEdit数据集，从生成相似度、用户满意度和质量进行评估。
英文摘要：
Generative AI evolves the execution of complex workflows in industry, where the large multimodal model empowers fashion design in the garment industry. Current generation AI models magically transform brainstorming into fancy designs easily, but the fine-grained customization still suffers from text uncertainty without professional background knowledge from end-users. Thus, we propose the Better Understanding Generation (BUG) workflow with LMM to automatically create and fine-grain customize the cloth designs from chat with image-into-prompt. Our framework unleashes users' creative potential beyond words and also lowers the barriers of clothing design/editing without further human involvement. To prove the effectiveness of our model, we propose a new FashionEdit dataset that simulates the real-world clothing design workflow, evaluated from generation similarity, user satisfaction, and quality. The code and dataset: https://github.com/detectiveli/FashionEdit.
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。
