Large scale 3D morphable models

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Title: Large scale 3D morphable models
Authors: Booth, JA
Roussos, A
Ponniah, A
Dunaway, D
Zafeiriou, S
Item Type: Journal Article
Abstract: We present large scale facial model (LSFM)—a 3D Morphable Model (3DMM) automatically constructed from 9663 distinct facial identities. To the best of our knowledge LSFM is the largest-scale Morphable Model ever constructed, containing statistical information from a huge variety of the human population. To build such a large model we introduce a novel fully automated and robust Morphable Model construction pipeline, informed by an evaluation of state-of-the-art dense correspondence techniques. The dataset that LSFM is trained on includes rich demographic information about each subject, allowing for the construction of not only a global 3DMM model but also models tailored for specific age, gender or ethnicity groups. We utilize the proposed model to perform age classification from 3D shape alone and to reconstruct noisy out-of-sample data in the low-dimensional model space. Furthermore, we perform a systematic analysis of the constructed 3DMM models that showcases their quality and descriptive power. The presented extensive qualitative and quantitative evaluations reveal that the proposed 3DMM achieves state-of-the-art results, outperforming existing models by a large margin. Finally, for the benefit of the research community, we make publicly available the source code of the proposed automatic 3DMM construction pipeline, as well as the constructed global 3DMM and a variety of bespoke models tailored by age, gender and ethnicity.
Issue Date: 8-Apr-2017
Date of Acceptance: 24-Mar-2017
DOI: 10.1007/s11263-017-1009-7
ISSN: 1573-1405
Publisher: Springer Verlag (Germany)
Start Page: 233
End Page: 254
Journal / Book Title: International Journal of Computer Vision
Volume: 126
Issue: 2-4
Copyright Statement: © The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Great Ormond Street Hospital for Children NHS Trust
Engineering & Physical Science Research Council (E
Funder's Grant Number: EP/J017787/1
R&D Number: 09SG10
Keywords: Science & Technology
Computer Science, Artificial Intelligence
Computer Science
3D morphable models
Dense correspondence
Demographic-specific models
0801 Artificial Intelligence And Image Processing
Artificial Intelligence & Image Processing
Publication Status: Published
Appears in Collections:Faculty of Engineering

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