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

TL;DR

HAIGEN is a collaborative AI system that streamlines fashion design by generating inspiration images from text, creating style-specific sketches, and coloring designs, thereby enhancing efficiency and protecting privacy.

Contribution

This work introduces HAIGEN, a novel human-AI collaborative system with cloud and local modules that improves fashion design workflows and safeguards user privacy.

Findings

Modules validated through extensive experiments

Significant improvements in design efficiency confirmed by user surveys

Effective integration of cloud and local models for privacy protection

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.