FIRST: A Million-Entry Dataset for Text-Driven Fashion Synthesis and Design

Huang, Zhen, Li, Yihao, Pei, Dong, Zhou, Jiapeng, Ning, Xuliang, Han, Jianlin, Han, Xiaoguang, Chen, Xuejun
{"title":"FIRST: A Million-Entry Dataset for Text-Driven Fashion Synthesis and\n Design","authors":"Huang, Zhen, Li, Yihao, Pei, Dong, Zhou, Jiapeng, Ning, Xuliang, Han, Jianlin, Han, Xiaoguang, Chen, Xuejun","doi":"10.48550/arxiv.2311.07414","DOIUrl":null,"url":null,"abstract":"Text-driven fashion synthesis and design is an extremely valuable part of artificial intelligence generative content(AIGC), which has the potential to propel a tremendous revolution in the traditional fashion industry. To advance the research on text-driven fashion synthesis and design, we introduce a new dataset comprising a million high-resolution fashion images with rich structured textual(FIRST) descriptions. In the FIRST, there is a wide range of attire categories and each image-paired textual description is organized at multiple hierarchical levels. Experiments on prevalent generative models trained over FISRT show the necessity of FIRST. We invite the community to further develop more intelligent fashion synthesis and design systems that make fashion design more creative and imaginative based on our dataset. The dataset will be released soon.","PeriodicalId":496270,"journal":{"name":"arXiv (Cornell University)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv (Cornell University)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.48550/arxiv.2311.07414","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Text-driven fashion synthesis and design is an extremely valuable part of artificial intelligence generative content(AIGC), which has the potential to propel a tremendous revolution in the traditional fashion industry. To advance the research on text-driven fashion synthesis and design, we introduce a new dataset comprising a million high-resolution fashion images with rich structured textual(FIRST) descriptions. In the FIRST, there is a wide range of attire categories and each image-paired textual description is organized at multiple hierarchical levels. Experiments on prevalent generative models trained over FISRT show the necessity of FIRST. We invite the community to further develop more intelligent fashion synthesis and design systems that make fashion design more creative and imaginative based on our dataset. The dataset will be released soon.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
第一:文本驱动时装合成与设计的百万条目数据集
文本驱动的时尚合成和设计是人工智能生成内容(AIGC)的一个非常有价值的部分,它有可能推动传统时尚产业的巨大革命。为了推进文本驱动的时尚合成和设计研究,我们引入了一个新的数据集,该数据集由一百万张高分辨率时尚图像组成,具有丰富的结构化文本(FIRST)描述。在FIRST中,有广泛的服装类别,每个图像配对的文本描述都是在多个层次上组织的。在常用的生成模型上进行的实验表明了FIRST的必要性。我们邀请社区进一步开发更智能的时装合成和设计系统,使时装设计基于我们的数据集更具创造性和想象力。数据集将于近期发布。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
CCD Photometry of the Globular Cluster NGC 5897 The Distribution of Sandpile Groups of Random Graphs with their Pairings CLiF-VQA: Enhancing Video Quality Assessment by Incorporating High-Level Semantic Information related to Human Feelings Full-dry Flipping Transfer Method for van der Waals Heterostructure Code-Aided Channel Estimation in LDPC-Coded MIMO Systems
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1