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

  • Di WU*
  • , Mingzhu WU
  • , Yeye LI
  • , Jianan JIANG
  • , Xinglin LI
  • , Hanhui DENG
  • , Can LIU
  • , Yi LI*
  • *Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

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. © 2024 ACM.
Original languageEnglish
Article number515
JournalProceedings of the ACM on Human-Computer Interaction
Volume8
Issue numberCSCW2
Online published8 Nov 2024
DOIs
Publication statusPublished - Nov 2024

Research Keywords

  • fashion design
  • federated learning
  • generative adversarial network
  • generative AI tool
  • sketch generation

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