The fusion of AI and fashion design has emerged as a promising research area. However, the lack of extensive, interrelated data on clothing and try-on stages has hindered the full potential of AI in this domain. Addressing this, we present the Fashion-Diffusion dataset, a product of multiple years' rigorous effort. This dataset, the first of its kind, comprises over a million high-quality fashion images, paired with detailed text descriptions. Sourced from a diverse range of geographical locations and cultural backgrounds, the dataset encapsulates global fashion trends. The images have been meticulously annotated with fine-grained attributes related to clothing and humans, simplifying the fashion design process into a Text-to-Image (T2I) task. The Fashion-Diffusion dataset not only provides high-quality text-image pairs and diverse human-garment pairs but also serves as a large-scale resource about humans, thereby facilitating research in T2I generation. Moreover, to foster standardization in the T2I-based fashion design field, we propose a new benchmark comprising multiple datasets for evaluating the performance of fashion design models. This work represents a significant leap forward in the realm of AI-driven fashion design, setting a new standard for future research in this field.

翻译：人工智能与时装设计的融合已成为一个极具前景的研究领域。然而，缺乏关于服装及试穿阶段的大规模、相互关联的数据，制约了人工智能在该领域的潜力。为此，我们提出了Fashion-Diffusion数据集，这是多年严谨工作的成果。作为首个此类数据集，它包含超过一百万张高质量时装图像，并配有详细的文本描述。数据来源涵盖了多样化的地理区域和文化背景，体现了全球时尚潮流。图像经过精细标注，附有与服装及人体相关的细粒度属性，从而将时装设计过程简化为文本到图（T2I）任务。Fashion-Diffusion数据集不仅提供了高质量文本-图像对和多样化的人体-服装对，还作为大规模人体相关资源，促进了T2I生成研究。此外，为促进基于T2I的时装设计领域的标准化，我们提出了一个新的基准，包含多个数据集，用于评估时装设计模型的性能。这项工作标志着人工智能驱动的时装设计领域取得了重大进展，为该领域的未来研究设立了新的标准。