HieraFashDiff：基于多阶段扩散模型的分层服装设计
HieraFashDiff: Hierarchical Fashion Design with Multi-stage Diffusion Models
AI总结：
针对现有服装生成方法未贴合实际设计流程的问题，提出分层扩散框架HieraFashDiff，通过构思-迭代两阶段模拟设计工作流，构建配套分层标注数据集，实现更高保真度与提示遵循性的服装设计生成与局部编辑。
摘要：
服装设计是一项具有挑战性的复杂过程。现有服装生成与编辑研究均未遵循实际服装设计流程，限制了其在实践中的应用。本文提出一种专为服装设计打造的新型分层扩散框架HieraFashDiff。该模型模拟实际服装设计工作流，将去噪过程拆解为两个连续阶段：1）构思阶段，根据高层概念生成设计方案；2）迭代阶段，利用低层属性持续优化设计方案。模型在单一框架内支持服装设计生成与细粒度局部编辑。为训练模型，我们构建了一个带有分层文本标注的全身服装图像新数据集。大量评估表明，与现有方法相比，本文方法生成的服装设计与编辑结果保真度更高、提示词遵循性更好，有望赋能实际服装设计工作流。代码与数据集可在https://github.com/haoli-zbdbc/hierafashdiff获取。
英文摘要：
Fashion design is a challenging and complex process.Recent works on fashion generation and editing are all agnostic of the actual fashion design process, which limits their usage in practice.In this paper, we propose a novel hierarchical diffusion-based framework tailored for fashion design, coined as HieraFashDiff. Our model is designed to mimic the practical fashion design workflow, by unraveling the denosing process into two successive stages: 1) an ideation stage that generates design proposals given high-level concepts and 2) an iteration stage that continuously refines the proposals using low-level attributes. Our model supports fashion design generation and fine-grained local editing in a single framework. To train our model, we contribute a new dataset of full-body fashion images annotated with hierarchical text descriptions. Extensive evaluations show that, as compared to prior approaches, our method can generate fashion designs and edited results with higher fidelity and better prompt adherence, showing its promising potential to augment the practical fashion design workflow. Code and Dataset are available at https://github.com/haoli-zbdbc/hierafashdiff.
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。
