Neural Style Transfer for Image-Based Garment Interchange Through Multi-Person Human Views

Hajer Ghodhbani, Mohamed Neji, Mohamed Neji, Adel Alimi, Adel Alimi

2023

Abstract

The generation of photorealistic images of human appearances under the guidance of body pose enables a wide range of applications, including virtual fitting and style synthesis. Several advances have been made in this direction using image-based deep learning generation approaches. The issue with these methods is that they produce significant aberrations in the final output, such as blurring of fine details and texture alterations. Our work falls within this objective by proposing a system able to realize the garment transfer between different views of person by overcoming these issues. To realize this objective, fundamental steps were achieved. Firstly, we used a conditioning adversarial network to deal with pose and appearance separately, create a human shape image with precise control over pose, and align target garment with appropriate body parts in the human image. As a second step, we introduced a neural approach for style transfer that can differentiate and merge content and style of editing images. We designed architecture with distinct levels to ensure the style transfer while preserving the quality of original texture in the generated results.

Paper Citation

in Harvard Style

Ghodhbani H., Neji M. and Alimi A. (2023). Neural Style Transfer for Image-Based Garment Interchange Through Multi-Person Human Views. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 327-335. DOI: 10.5220/0011694200003417

in Bibtex Style

@conference{visapp23,
author={Hajer Ghodhbani and Mohamed Neji and Adel Alimi},
title={Neural Style Transfer for Image-Based Garment Interchange Through Multi-Person Human Views},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={327-335},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011694200003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - Neural Style Transfer for Image-Based Garment Interchange Through Multi-Person Human Views
SN - 978-989-758-634-7
AU - Ghodhbani H.
AU - Neji M.
AU - Alimi A.
PY - 2023
SP - 327
EP - 335
DO - 10.5220/0011694200003417
PB - SciTePress