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

作者：Di Wu, Mingzhu Wu, Yeye Li, Jianan Jiang, Xinglin Li, Hanhui Deng, Can Liu, Yi Li · 发表于：Proceedings of the ACM on Human-Computer Interaction · 年份：2024 · DOI：10.1145/3687054 · 被引用次数：6 · 研究领域：Fashion and Cultural Textiles、Generative Adversarial Networks and Image Synthesis、3D Shape Modeling and Analysis

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.