HAIGEN: Towards Human-AI Collaboration for Facilitating Creativity and Style Generation in Fashion Design
<|reference_start|>HAIGEN: Towards Human-AI Collaboration for Facilitating Creativity and Style Generation in Fashion Design: 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.<|reference_end|>
@article{jiang2024haigen:,
    title={HAIGEN: Towards Human-AI Collaboration for Facilitating Creativity and
  Style Generation in Fashion Design},
    author={Jianan Jiang, Di Wu, Hanhui Deng, Yidan Long, Wenyi Tang, Xiang Li,
  Can Liu, Zhanpeng Jin, Wenlei Zhang, Tangquan Qi},
    journal={arXiv preprint arXiv:2408.00855},
    year={2024},
    doi={10.1145/3678518},
    archivePrefix={arXiv},
    eprint={2408.00855},
    primaryClass={cs.HC}
}