research-article

HAIGEN: Towards Human-AI Collaboration for Facilitating Creativity and Style Generation in Fashion Design

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Tangquan QiAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 8, Issue 3

Article No.: 107, Pages 1 - 27

https://doi.org/10.1145/3678518

Published: 09 September 2024 Publication History

Abstract

The process of fashion design usually involves sketching, refining, and coloring, with designers drawing inspiration from various images to fuel their creative endeavors. However, conventional image search methods often yield irrelevant results, impeding the design process. Moreover, creating and coloring sketches can be time-consuming and demanding, acting as a bottleneck in the design workflow. In this work, we introduce HAIGEN (Human-AI Collaboration for GENeration), an efficient fashion design system for Human-AI collaboration developed to aid designers. Specifically, HAIGEN consists of four modules. T2IM, located in the cloud, generates reference inspiration images directly from text prompts. With three other modules situated locally, the I2SM batch generates the image material library into a certain designer-style sketch material library. The SRM recommends similar sketches in the generated library to designers for further refinement, and the STM colors the refined sketch according to the styles of inspiration images. Through our system, any designer can perform local personalized fine-tuning and leverage the powerful generation capabilities of large models in the cloud, streamlining the entire design development process. Given that our approach integrates both cloud and local model deployment schemes, it effectively safeguards design privacy by avoiding the need to upload personalized data from local designers. We validated the effectiveness of each module through extensive qualitative and quantitative experiments. User surveys also confirmed that HAIGEN offers significant advantages in design efficiency, positioning it as a new generation of aid-tool for designers.

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Cited By

Wu DWu MLi YJiang JLi XDeng HLiu CLi Y(2024)StyleWe: Towards Style Fusion in Generative Fashion Design with Efficient Federated AIProceedings of the ACM on Human-Computer Interaction10.1145/36870548:CSCW2(1-31)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3687054
Nik Harun NMd Syed MAbdullah F(2024)Revolutionising costume design education: a case study on the impact of Microsoft CopilotQuality Assurance in Education10.1108/QAE-06-2024-0119Online publication date: 13-Dec-2024
https://doi.org/10.1108/QAE-06-2024-0119
Bieńkowska J(2024)The Effects of Artificial Intelligence on the Fashion Industry—Opportunities and Challenges for Sustainable TransformationSustainable Development10.1002/sd.3312Online publication date: 26-Dec-2024
https://doi.org/10.1002/sd.3312

Index Terms

HAIGEN: Towards Human-AI Collaboration for Facilitating Creativity and Style Generation in Fashion Design

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 8, Issue 3

September 2024

1782 pages

EISSN:2474-9567

DOI:10.1145/3695755

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Published: 09 September 2024

Published in IMWUT Volume 8, Issue 3

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Cited By

Wu DWu MLi YJiang JLi XDeng HLiu CLi Y(2024)StyleWe: Towards Style Fusion in Generative Fashion Design with Efficient Federated AIProceedings of the ACM on Human-Computer Interaction10.1145/36870548:CSCW2(1-31)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3687054
Nik Harun NMd Syed MAbdullah F(2024)Revolutionising costume design education: a case study on the impact of Microsoft CopilotQuality Assurance in Education10.1108/QAE-06-2024-0119Online publication date: 13-Dec-2024
https://doi.org/10.1108/QAE-06-2024-0119
Bieńkowska J(2024)The Effects of Artificial Intelligence on the Fashion Industry—Opportunities and Challenges for Sustainable TransformationSustainable Development10.1002/sd.3312Online publication date: 26-Dec-2024
https://doi.org/10.1002/sd.3312

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