EasyFashion: A Human-AI Co-Creation System for Personalized Fashion Design and Sewing Pattern Generation

Hong Qu1, Zhaoxiang Xu1, Jinbo Luo1, Yujie Zhao2, Jie Zhang3,*, Yadie Yang1,*
1The School of Fashion and Textiles, The Hong Kong Polytechnic University
2The School of Arts and Design, University of Sanya
3Faculty of Applied Sciences, Macao Polytechnic University
*Co-corresponding authors
arXiv preprint, 2026
Paper arXiv Video Interactive Demo Code: Under preparation
Four-stage user journey in the EasyFashion system

EasyFashion supports the complete journey from multimodal design intent and personal body photos to iterative virtual try-on, sewing patterns, and physical garment production.

Abstract

People often want garments that reflect their aesthetic preferences, fit their bodies, and meet their sizing needs, yet turning these requirements into physical garments remains difficult. Ready-to-wear options provide limited personalization, while custom tailoring is costly and time-consuming. Recent generative artificial intelligence (AI) systems can visualize garment ideas but often stop short of supporting downstream production. To address this gap, we present EasyFashion, a human-AI co-creation system that enables users to iteratively refine design intent for personalized garment style and size, evaluate designs through virtual try-on on reconstructed personal avatars, and generate sewing patterns for garment production. Using reference images, text descriptions, and body photos as input, EasyFashion translates user intent into structured garment specifications and try-on results. Technical experiments, user studies, and a real-world production case demonstrate the value of EasyFashion for multimodal design expression, body-specific evaluation, and production-oriented outputs in personalized garment design.

Video

Overview

Internal pipeline of the EasyFashion system
Overview of the EasyFashion pipeline. The input module receives front and side body photos, a style reference image, and design requirements. Human body reconstruction creates a personalized avatar; the Fashion Agent translates design intent into an editable garment specification and appearance guidance; downstream modules generate digital fabric and sewing patterns; and rendering and simulation produce body-specific front and back views.

Results

EasyFashion results across participants with different body shapes and garment styles
EasyFashion examples across participants with diverse body shapes and garment styles. The system connects personalized avatars and virtual try-on results with production-oriented sewing patterns and body measurements.

BibTeX

@article{qu2026easyfashion,
  title={EasyFashion: A Human-AI Co-Creation System for Personalized Fashion Design and Sewing Pattern Generation},
  author={Qu, Hong and Xu, Zhaoxiang and Luo, Jinbo and Zhao, Yujie and Zhang, Jie and Yang, Yadie},
  journal={arXiv preprint arXiv:2609.18483},
  year={2026},
  eprint={2609.18483},
  archivePrefix={arXiv},
  primaryClass={cs.HC},
  url={https://arxiv.org/abs/2609.18483}
}