By the same authors

A Human Ear Reconstruction Autoencoder: HERA

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Standard

A Human Ear Reconstruction Autoencoder : HERA. / Sun, Hao; Pears, Nicholas Edwin; Dai, Hang.

16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications: VISIGRAPP 2021. Vol. 5 SCITEPRESS – Science and Technology Publications, 2021. p. 136-145.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Harvard

Sun, H, Pears, NE & Dai, H 2021, A Human Ear Reconstruction Autoencoder: HERA. in 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications: VISIGRAPP 2021. vol. 5, SCITEPRESS – Science and Technology Publications, pp. 136-145, 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications., 8/02/21.

APA

Sun, H., Pears, N. E., & Dai, H. (2021). A Human Ear Reconstruction Autoencoder: HERA. In 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications: VISIGRAPP 2021 (Vol. 5, pp. 136-145). SCITEPRESS – Science and Technology Publications.

Vancouver

Sun H, Pears NE, Dai H. A Human Ear Reconstruction Autoencoder: HERA. In 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications: VISIGRAPP 2021. Vol. 5. SCITEPRESS – Science and Technology Publications. 2021. p. 136-145

Author

Sun, Hao ; Pears, Nicholas Edwin ; Dai, Hang. / A Human Ear Reconstruction Autoencoder : HERA. 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications: VISIGRAPP 2021. Vol. 5 SCITEPRESS – Science and Technology Publications, 2021. pp. 136-145

Bibtex - Download

@inproceedings{68da790dc53448aa95d4235bfee44ca0,
title = "A Human Ear Reconstruction Autoencoder: HERA",
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.",
keywords = "Ear, 3D ear model, 3D morphable model, 3D reconstruction, Self-supervised learning, Autoencoder",
author = "Hao Sun and Pears, {Nicholas Edwin} and Hang Dai",
year = "2021",
month = feb,
day = "8",
language = "English",
volume = "5",
pages = "136--145",
booktitle = "16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications",
publisher = "SCITEPRESS – Science and Technology Publications",
note = "16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications., VISAPP 2021 ; Conference date: 08-02-2021 Through 10-02-2021",
url = "http://www.visapp.visigrapp.org/Home.aspx",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - A Human Ear Reconstruction Autoencoder

T2 - 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications.

AU - Sun, Hao

AU - Pears, Nicholas Edwin

AU - Dai, Hang

PY - 2021/2/8

Y1 - 2021/2/8

N2 - 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.

AB - 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.

KW - Ear, 3D ear model, 3D morphable model, 3D reconstruction, Self-supervised learning, Autoencoder

M3 - Conference contribution

VL - 5

SP - 136

EP - 145

BT - 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

PB - SCITEPRESS – Science and Technology Publications

Y2 - 8 February 2021 through 10 February 2021

ER -

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