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Statistical Modeling of Craniofacial Shape and Texture

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JournalInternational Journal of Computer Vision
DateAccepted/In press - 28 Oct 2019
DateE-pub ahead of print - 9 Nov 2019
DatePublished (current) - Feb 2020
Volume128
Number of pages25
Pages (from-to)547–571
Early online date9/11/19
Original languageEnglish

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.

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© The Author(s) 2019

    Research areas

  • 3D morphable model; Statistical shape model; Craniofacial shape; Shape morphing

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