Quality and Quantity: Unveiling a Million High-Quality Images for Text-to-Image Synthesis in Fashion Design
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866911800058970112 |
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| author | Yu, Jia Zhang, Lichao Chen, Zijie Pan, Fayu Wen, MiaoMiao Yan, Yuming Weng, Fangsheng Zhang, Shuai Pan, Lili Lan, Zhenzhong |
| author_facet | Yu, Jia Zhang, Lichao Chen, Zijie Pan, Fayu Wen, MiaoMiao Yan, Yuming Weng, Fangsheng Zhang, Shuai Pan, Lili Lan, Zhenzhong |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2311_12067 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Quality and Quantity: Unveiling a Million High-Quality Images for Text-to-Image Synthesis in Fashion Design Yu, Jia Zhang, Lichao Chen, Zijie Pan, Fayu Wen, MiaoMiao Yan, Yuming Weng, Fangsheng Zhang, Shuai Pan, Lili Lan, Zhenzhong Computer Vision and Pattern Recognition 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. |
| title | Quality and Quantity: Unveiling a Million High-Quality Images for Text-to-Image Synthesis in Fashion Design |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2311.12067 |