StyleWe: Towards Style Fusion in Generative Fashion Design with Efficient Federated AI

Collaboration can amalgamate diverse ideas, styles, and visual elements, fostering creativity and innovation among different designers. In collaborative design, sketches play a pivotal role as a means of expressing design creativity. However, designers often tend to not openly share these meticulously crafted sketches. This phenomenon of data island in the design area hinders its digital transformation under the third wave of AI. In this paper, we introduce a Federated Generative Artificial Intelligence Clothing system, namely StyleWe, employing federated learning to aid in sketch design. StyleWe is committed to establishing an ecosystem wherein designers can exchange sketch styles among themselves. Through StyleWe, designers can generate sketches that incorporate various designers' styles from their peers, drawing inspiration from collaboration without the need for data disclosure or upload. Extensive performance evaluations and user studies indicate that our StyleWe system can produce multi-styled sketches of comparable quality to human-designed ones while significantly enhancing efficiency compared to hand-drawn sketches.

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Publication Details

Journal
Proceedings of the ACM on Human-Computer Interaction
Published
2024-11-07
DOI
https://doi.org/10.1145/3687054
Citations
4
Primary Topic
Fashion and Cultural Textiles
Type
article
Field-Weighted Citation Impact
20.22
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article

StyleWe: Towards Style Fusion in Generative Fashion Design with Efficient Federated AI

Jianan Jiang, Xinglin Li, Di Wu, Yeye Li et al.
4 citations
Proceedings of the ACM on Human-Computer Interaction
Fashion and Cultural Textiles
20.22
article

StyleWe: Towards Style Fusion in Generative Fashion Design with Efficient Federated AI

Jianan Jiang, Xinglin Li, Di Wu, Yeye Li, Hanhui Deng, Can Liu, Yi Li, Mingzhu Wu
article en
4 citations

Abstract

Collaboration can amalgamate diverse ideas, styles, and visual elements, fostering creativity and innovation among different designers. In collaborative design, sketches play a pivotal role as a means of expressing design creativity. However, designers often tend to not openly share these meticulously crafted sketches. This phenomenon of data island in the design area hinders its digital transformation under the third wave of AI. In this paper, we introduce a Federated Generative Artificial Intelligence Clothing system, namely StyleWe, employing federated learning to aid in sketch design. StyleWe is committed to establishing an ecosystem wherein designers can exchange sketch styles among themselves. Through StyleWe, designers can generate sketches that incorporate various designers' styles from their peers, drawing inspiration from collaboration without the need for data disclosure or upload. Extensive performance evaluations and user studies indicate that our StyleWe system can produce multi-styled sketches of comparable quality to human-designed ones while significantly enhancing efficiency compared to hand-drawn sketches.

Proceedings of the ACM on Human-Computer InteractionVol. 8(CSCW2)
Hunan University (CN), City University of Hong Kong (HK), Hunan Academy of Traditional Chinese Medicine (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 1%
Fashion and Cultural Textiles
20.22
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StyleWe: Towards Style Fusion in Generative Fashion Design with Efficient Federated AI — Jianan Jiang, Xinglin Li, et al. · Proceedings of the ACM on Human-Computer Interaction (2024) | TGRS Research Map | TGRS