FaceVision-GAN: A 3D Model Face Reconstruction Method from a Single Image Using GANs

Danilo Avola, Luigi Cinque, Gian Luca Foresti, Marco Raoul Marini

2024

Abstract

Generative algorithms have been very successful in recent years. This phenomenon derives from the strong computational power that even consumer computers can provide. Moreover, a huge amount of data is available today for feeding deep learning algorithms. In this context, human 3D face mesh reconstruction is becoming an important but challenging topic in computer vision and computer graphics. It could be exploited in different application areas, from security to avatarization. This paper provides a 3D face reconstruction pipeline based on Generative Adversarial Networks (GANs). It can generate high-quality depth and correspondence maps from 2D images, which are exploited for producing a 3D model of the subject’s face.

Paper Citation

in Harvard Style

Avola D., Cinque L., Luca Foresti G. and Raoul Marini M. (2024). FaceVision-GAN: A 3D Model Face Reconstruction Method from a Single Image Using GANs. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 628-632. DOI: 10.5220/0012306200003654

in Bibtex Style

@conference{icpram24,
author={Danilo Avola and Luigi Cinque and Gian Luca Foresti and Marco Raoul Marini},
title={FaceVision-GAN: A 3D Model Face Reconstruction Method from a Single Image Using GANs},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2024},
pages={628-632},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012306200003654},
isbn={978-989-758-684-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - FaceVision-GAN: A 3D Model Face Reconstruction Method from a Single Image Using GANs
SN - 978-989-758-684-2
AU - Avola D.
AU - Cinque L.
AU - Luca Foresti G.
AU - Raoul Marini M.
PY - 2024
SP - 628
EP - 632
DO - 10.5220/0012306200003654
PB - SciTePress