Dai, Hang, Pears, Nicholas Edwin orcid.org/0000-0001-9513-5634, Smith, William Alfred Peter orcid.org/0000-0002-6047-0413 et al. (1 more author) (2020) Statistical Modeling of Craniofacial Shape and Texture. International Journal of Computer Vision. 547–571. ISSN 0920-5691
Abstract
We present a fully-automatic statistical 3D shape modeling approach and apply it to a large dataset of 3D images, the Headspace dataset, thus generating the first public shape-and-texture 3D Morphable Model (3DMM) of the full human head. Our approach is the first to employ a template that adapts to the dataset subject before dense morphing. This is fully automatic and achieved using 2D facial landmarking, projection to 3D shape, and mesh editing. In dense template morphing, we improve on the well-known Coherent Point Drift algorithm, by incorporating iterative data-sampling and alignment. Our evaluations demonstrate that our method has better performance in correspondence accuracy and modeling ability when compared with other competing algorithms. We propose a texture map refinement scheme to build high quality texture maps and texture model. We present several applications that include the first clinical use of craniofacial 3DMMs in the assessment of different types of surgical intervention applied to a craniosynostosis patient group.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2019 |
Keywords: | 3D morphable model; Statistical shape model; Craniofacial shape; Shape morphing |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 29 Oct 2019 16:20 |
Last Modified: | 21 Jan 2025 17:42 |
Published Version: | https://doi.org/10.1007/s11263-019-01260-7 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1007/s11263-019-01260-7 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:152781 |
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