Large Scale 3D Morphable Models

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 populati...

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Vydáno v:International journal of computer vision Ročník 126; číslo 2-4; s. 233 - 254
Hlavní autoři: Booth, James, Roussos, Anastasios, Ponniah, Allan, Dunaway, David, Zafeiriou, Stefanos
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.04.2018
Springer
Springer Nature B.V
Témata:
3-D technology
Age
Artificial Intelligence
Automation
Computer Imaging
Computer Science
Demographics
Ethnicity
Image Processing and Computer Vision
Pattern Recognition
Pattern Recognition and Graphics
Pipeline construction
Population (statistical)
Scale (ratio)
State of the art
Three dimensional models
Vision
Well construction
3D morphable models
Demographic-specific models
Dense correspondence
ISSN:0920-5691, 1573-1405
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Shrnutí: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.
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Communicated by Edmond Boyer, Cordelia Schmid.
ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-017-1009-7