3D Virtual Fitting Network (3D VFN)

Danyal Mahmood, Wei Leong, Humaira Nisar, Ahmad Mazlan

2024

Abstract

With the rise in digital technology and the fast pace of life, as well as the change in lifestyle due to the pandemic, people have started adopting online shopping in the garment industry as well. Hence, research on Virtual Try-On (VTO) technologies to be implemented in virtual fitting rooms (VFRs) has drawn significant attention. The existing VFR technologies rely on deep generative models with an end-to-end pipeline, from feature extraction to garment warping and refinement. While currently there are 2D and 3D VTO solutions, the 3D ones have enormous commercial potential in the fashion market as the technology has been proven effective for providing a photo-realistic and detailed try-on result. However, the existing 3D VTO solutions principally rely on annotated human body shapes or avatars, which are unrealistic. By integrating the technologies embedded in both 2D and 3D VTO solutions, this paper proposes a VTO solution that relies on geometric settings in the 3D space namely the 3D Virtual Fitting Network (3D VFN), that solely relies on 2D RGB garment and single-person human images as inputs, generating a photo-realistic warped garment output image by utilizing the geometric settings in the 3D space.

Paper Citation

in Harvard Style

Mahmood D., Leong W., Nisar H. and Mazlan A. (2024). 3D Virtual Fitting Network (3D VFN). In Proceedings of the 4th International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE; ISBN 978-989-758-693-4, SciTePress, pages 91-98. DOI: 10.5220/0012726900003720

in Bibtex Style

@conference{improve24,
author={Danyal Mahmood and Wei Leong and Humaira Nisar and Ahmad Mazlan},
title={3D Virtual Fitting Network (3D VFN)},
booktitle={Proceedings of the 4th International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE},
year={2024},
pages={91-98},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012726900003720},
isbn={978-989-758-693-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE
TI - 3D Virtual Fitting Network (3D VFN)
SN - 978-989-758-693-4
AU - Mahmood D.
AU - Leong W.
AU - Nisar H.
AU - Mazlan A.
PY - 2024
SP - 91
EP - 98
DO - 10.5220/0012726900003720
PB - SciTePress