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Authors: Hao Sun 1 ; Nick Pears 1 and Hang Dai 2

Affiliations: 1 Department of Computer Science, University of York, York, U.K. ; 2 Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, U.A.E.

Keyword(s): Ear, 3D Ear Model, 3D Morphable Model, 3D Reconstruction, Self-supervised Learning, Autoencoder.

Abstract: The ear, as an important part of the human head, has received much less attention compared to the human face in the area of computer vision. Inspired by previous work on monocular 3D face reconstruction using an autoencoder structure to achieve self-supervised learning, we aim to utilise such a framework to tackle the 3D ear reconstruction task, where more subtle and difficult curves and features are present on the 2D ear input images. Our Human Ear Reconstruction Autoencoder (HERA) system predicts 3D ear poses and shape parameters for 3D ear meshes, without any supervision to these parameters. To make our approach cover the variance for in-the-wild images, even grayscale images, we propose an in-the-wild ear colour model. The constructed end-to-end self-supervised model is then evaluated both with 2D landmark localisation performance and the appearance of the reconstructed 3D ears.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Sun, H.; Pears, N. and Dai, H. (2021). A Human Ear Reconstruction Autoencoder. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP; ISBN 978-989-758-488-6; ISSN 2184-4321, SciTePress, pages 136-145. DOI: 10.5220/0010249901360145

@conference{visapp21,
author={Hao Sun. and Nick Pears. and Hang Dai.},
title={A Human Ear Reconstruction Autoencoder},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP},
year={2021},
pages={136-145},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010249901360145},
isbn={978-989-758-488-6},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP
TI - A Human Ear Reconstruction Autoencoder
SN - 978-989-758-488-6
IS - 2184-4321
AU - Sun, H.
AU - Pears, N.
AU - Dai, H.
PY - 2021
SP - 136
EP - 145
DO - 10.5220/0010249901360145
PB - SciTePress