首个面向完整穿搭的虚拟试穿数据集，支持多服饰与配饰精细还原。
Garments2Look: A Multi-Reference Dataset for High-Fidelity Outfit-Level Virtual Try-On with Clothing and Accessories
- 构建包含80K穿搭对的多参考数据集，每套穿搭含3-12件服装
- 现有方法在完整穿搭试穿中易出现错层与图像失真，准确率不足
- 适合研究穿搭生成、跨模态对齐及虚拟时尚系统的开发者
虚拟试穿（VTON）已能实现单件服饰的可视化，但真实时尚以多件服装、配饰、细分类别、叠穿和多样化搭配为核心，当前系统仍难以处理。现有数据集类别有限，缺乏穿搭多样性。我们提出Garments2Look，首个大规模多模态穿搭级虚拟试穿数据集，包含80,000个「多服饰→一穿搭」样本，覆盖40大类、300多个细分类别。每对样本包括3-12件参考服装（平均4.48件）、穿戴者图像及详尽物品与试穿文本标注。通过启发式构建穿搭列表并结合自动化过滤与人工验证，确保数据真实性与多样性。我们适配前沿VTON与通用图像编辑模型建立基线，结果表明当前方法在完整穿搭试穿中难以实现无缝衔接，常出现层序错误与伪影。
原文摘要 · Abstract (English)
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.
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