Statistical modeling of craniofacial shape and texture

Dai, H., Pears, N., Smith, W. and Duncan, C. (2020) Statistical modeling of craniofacial shape and texture. International Journal of Computer Vision, 128(2), pp. 547-571. (doi: 10.1007/s11263-019-01260-7)

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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Dai, Dr Hang
• Authors: Dai, H., Pears, N., Smith, W., and Duncan, C.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: International Journal of Computer Vision
• Publisher: Springer
• ISSN: 0920-5691
• ISSN (Online): 1573-1405
• Published Online: 09 November 2019
• Copyright Holders: Copyright © 2019 The Author(s)
• First Published: First published in International Journal of Computer Vision 128(2):547-571
• Publisher Policy: Reproduced under a Creative Commons licence