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Authors: Danilo Avola 1 ; Luigi Cinque 1 ; Gian Luca Foresti 2 and Marco Raoul Marini 1

Affiliations: 1 Sapienza, University of Rome, Department of Computer Science, Via Salaria 113, 00199, Rome, Italy ; 2 University of Udine, Department of Mathematics, Computer Science and Physics, Via delle Scienze 206, 33100 Udine, Italy

Keyword(s): GAN, 2D to 3D Reconstruction, Face Syntesis, 3D Modelling from Single Image.

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.

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Paper citation in several formats:
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 - ICPRAM; ISBN 978-989-758-684-2; ISSN 2184-4313, SciTePress, pages 628-632. DOI: 10.5220/0012306200003654

@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 - ICPRAM},
year={2024},
pages={628-632},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012306200003654},
isbn={978-989-758-684-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - FaceVision-GAN: A 3D Model Face Reconstruction Method from a Single Image Using GANs
SN - 978-989-758-684-2
IS - 2184-4313
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