EditGarment：一个基于指令的服装编辑数据集，通过自动化MLLM合成与语义感知评估构建
EditGarment: An Instruction-Based Garment Editing Dataset Constructed with Automated MLLM Synthesis and Semantic-Aware Evaluation
AI总结：
提出一种自动化流水线构建首个基于指令的服装编辑数据集EditGarment，通过定义六类编辑指令和引入语义感知评估指标Fashion Edit Score，从52,257个候选三元组中筛选出20,596个高质量数据，解决了高质量指令-图像对稀缺的问题。
摘要：
基于指令的服装编辑通过自然语言实现精确的图像修改，在时尚设计和定制中具有广泛应用。与一般编辑任务不同，它需要理解服装特定的语义和属性依赖关系。然而，高质量指令-图像对的稀缺限制了其进展，因为人工标注成本高且难以扩展。尽管MLLM在自动化数据合成方面展现出潜力，但其在服装编辑中的应用受限于不精确的指令建模和缺乏时尚特定的监督信号。为解决这些挑战，我们提出了一种用于构建服装编辑数据集的自动化流水线。首先，我们定义了六类与真实时尚工作流对齐的编辑指令类别，以指导生成平衡且多样的指令-图像三元组。其次，我们引入Fashion Edit Score，这是一种语义感知评估指标，能够捕捉服装属性间的语义依赖关系，并在构建过程中提供可靠监督。使用该流水线，我们共构建了52,257个候选三元组，并保留了20,596个高质量三元组以构建EditGarment，这是首个专为独立服装编辑定制的基于指令的数据集。项目页面为https://yindq99.github.io/EditGarment-project/。
英文摘要：
Instruction-based garment editing enables precise image modifications via natural language, with broad applications in fashion design and customization. Unlike general editing tasks, it requires understanding garment-specific semantics and attribute dependencies. However, progress is limited by the scarcity of high-quality instruction-image pairs, as manual annotation is costly and hard to scale. While MLLMs have shown promise in automated data synthesis, their application to garment editing is constrained by imprecise instruction modeling and a lack of fashion-specific supervisory signals. To address these challenges, we present an automated pipeline for constructing a garment editing dataset. We first define six editing instruction categories aligned with real-world fashion workflows to guide the generation of balanced and diverse instruction-image triplets. Second, we introduce Fashion Edit Score, a semantic-aware evaluation metric that captures semantic dependencies between garment attributes and provides reliable supervision during construction. Using this pipeline, we construct a total of 52,257 candidate triplets and retain 20,596 high-quality triplets to build EditGarment, the first instruction-based dataset tailored to standalone garment editing. The project page is https://yindq99.github.io/EditGarment-project/.
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。
