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arXiv · 2311.12067

Quality and Quantity: Unveiling a Million High-Quality Images for Text-to-Image Synthesis in Fashion Design

Jia Yu·Lichao Zhang·Zijie Chen·Fayu Pan·MiaoMiao Wen·Yuming Yan·Fangsheng Weng·Shuai Zhang·Lili Pan·Zhenzhong Lan
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Abstract

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.

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Jia Yu, Lichao Zhang, Zijie Chen, Fayu Pan, MiaoMiao Wen, Yuming Yan, Fangsheng Weng, Shuai Zhang, Lili Pan, Zhenzhong Lan. 2024-03-18. Quality and Quantity: Unveiling a Million High-Quality Images for Text-to-Image Synthesis in Fashion Design. https://arxiv.org/abs/2311.12067

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