Garments2Look：面向含服装与配饰的高保真整套穿搭虚拟试衣的多参考数据集
Garments2Look: A Multi-Reference Dataset for High-Fidelity Outfit-Level Virtual Try-On with Clothing and Accessories
为多参考服装与配饰的整套穿搭试衣提供了 8 万对多图到单图的配对数据与基线评测，做可控生成或多参考条件注入的研究者可直接用它检验模型在层叠、细粒度类别与造型推理上的能力。
原文依据 We introduce Garments2Look, the first large-scale multimodal dataset for outfit-level VTON
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论虚拟试衣（VTON）已在单件服装可视化上取得进展，但现实时尚以整套穿搭为核心，涉及多件服装、配饰、细粒度类别、层叠关系与多样造型，这些仍超出当前 VTON 系统的能力范围。现有数据集类别有限且缺乏穿搭多样性。我们提出 Garments2Look，这是首个面向整套穿搭级 VTON 的大规模多模态数据集，包含 8 万对多服装到单套造型的配对样本，覆盖 40 个主要类别与 300 多个细粒度子类别。每对样本包含一套穿搭的 3 至 12 张参考服装图像（平均 4.48 张）、一张穿着该套穿搭的模特图像，以及详细的单品与试衣文本标注。为兼顾真实性与多样性，我们提出一套合成流程：先以启发式方法构建穿搭清单，再生成试衣结果，整个过程经过严格的自动过滤与人工校验以保证数据质量。为探查任务难度，我们将当前最优的 VTON 方法与通用图像编辑模型适配为该任务的基线。结果表明，现有方法难以无缝地试穿整套穿搭，也难以推断正确的层叠关系与造型，从而导致错位与伪影。
英文原文摘要 1219 字符
Virtual try-on (VTON) has advanced single-garment visualization, yet real-world fashion centers on full outfits with multiple garments, accessories, fine-grained categories, layering, and diverse styling, remaining beyond current VTON systems. Existing datasets are category-limited and lack outfit diversity. We introduce Garments2Look, the first large-scale multimodal dataset for outfit-level VTON, comprising 80K many-garments-to-one-look pairs across 40 major categories and 300+ fine-grained subcategories. Each pair includes an outfit with 3-12 reference garment images (Average 4.48), a model image wearing the outfit, and detailed item and try-on textual annotations. To balance authenticity and diversity, we propose a synthesis pipeline. It involves heuristically constructing outfit lists before generating try-on results, with the entire process subjected to strict automated filtering and human validation to ensure data quality. To probe task difficulty, we adapt SOTA VTON methods and general-purpose image editing models to establish baselines. Results show current methods struggle to try on complete outfits seamlessly and to infer correct layering and styling, leading to misalignment and artifacts.