Apparel is essential to human life, offering protection, mirroring cultural identities, and showcasing personal style. Yet, the creation of garments remains a time-consuming process, largely due to the manual work involved in designing them. To simplify this process, we introduce AIpparel, a multimodal foundation model for generating and editing sewing patterns. Our model fine-tunes state-of-the-art large multimodal models (LMMs) on a custom-curated large-scale dataset of over 120,000 unique garments, each with multimodal annotations including text, images, and sewing patterns. Additionally, we propose a novel tokenization scheme that concisely encodes these complex sewing patterns so that LLMs can learn to predict them efficiently. AIpparel achieves state-of-the-art performance in single-modal tasks, including text-to-garment and image-to-garment prediction, and enables novel multimodal garment generation applications such as interactive garment editing. The project website is at https://georgenakayama.github.io/AIpparel/.
@article{arxiv.2412.03937,
title = {AIpparel: A Multimodal Foundation Model for Digital Garments},
author = {Kiyohiro Nakayama and Jan Ackermann and Timur Levent Kesdogan and Yang Zheng and Maria Korosteleva and Olga Sorkine-Hornung and Leonidas J. Guibas and Guandao Yang and Gordon Wetzstein},
journal= {arXiv preprint arXiv:2412.03937},
year = {2025}
}
Comments
The project website is at https://georgenakayama.github.io/AIpparel/