Symmetric Shape Morphing for 3D Face and Head Modelling

Dai, Hang; Pears, Nick; Smith, William A. P.; Duncan, Christian A.

doi:10.1109/fg.2018.00023

Cited by 8 publications

(8 citation statements)

References 33 publications

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“…9.2, we demonstrate that the new morphing results are superior to our earlier approach and we update our LYHM 3DMM public release with a version that uses this improved morphing. Our other earlier work introduced a symmetrized version of the CPD morphing algorithm (Dai et al 2018a), and we also evaluate against this pipeline here, although symmetry is not a central consideration of this paper. Furthermore, in this paper, we use the constructed 3DMM in the first 3DMM-based clinical assessment of craniofacial surgery.…”

Section: Related Workmentioning

confidence: 99%

“…9.2, we compare three registration methods: i. the proposed adaptive template and ICPD based approach, ii. our recent symmetric deformation algorithm (Dai et al 2018b), and iii. our earlier hierarchical parts-based morphing method (Dai et al 2017b).…”

Section: Evaluation Of Correspondencesmentioning

confidence: 99%

“…This test is conducted on 100 subjects with neutral expression from the BU3D dataset Fig. 16 Correspondence performance for the proposed method compared to hierarchical parts-based CPD-LB (Dai et al 2017b) and symmetry-aware CPD (Dai et al 2018a). 1Mean per-vertex closest point error;…”

Section: Ablation Studymentioning

confidence: 99%

“…This section compares the proposed method with our previous work. Figure 16 compares: (i) the proposed ICPD with adaptive template, (ii) hierarchical parts-based CPD-LB (Dai et al 2017b), and (iii) symmetry-aware CPD (Dai et al 2018a).…”

Section: Comparison With Previous Workmentioning

confidence: 99%

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Statistical Modeling of Craniofacial Shape and Texture

et al. 2019

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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. Keywords 3D morphable model • Statistical shape model • Craniofacial shape • Shape morphing 1 Introduction Very young children quickly learn to understand the rich shape and texture variation in a certain class of object, such as human faces, cats or chairs, in both 2D and 3D. This ability helps them to recognize the same person, distinguish different kinds of creatures, and sketch unseen samples of the same object class. In machine learning, the process of capturing this prior knowledge is mathematically interpreted as statistical modeling. One such realisation of this is a 3D Morphable Model (3DMM) (Blanz and Vetter 1999), a vector space representation of objects, that captures the variation of Communicated by Cristian Sminchisescu.

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Section: Related Workmentioning

confidence: 99%

Section: Evaluation Of Correspondencesmentioning

confidence: 99%

Section: Ablation Studymentioning

confidence: 99%

Section: Comparison With Previous Workmentioning

confidence: 99%

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Statistical Modeling of Craniofacial Shape and Texture

et al. 2019

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“…Myronenko et al consider the alignment of two point sets as a probability density estimation [15] and they call the method Coherent Point Drift (CPD). The CPD approach is extended in [22], [23], [24], [25]. Dai et al proposed a hierarchical parts-based CPD-LB morphing framework to avoid underfitting and over-fitting [14].…”

Section: Related Workmentioning

confidence: 99%

A Data-Augmented 3D Morphable Model of the Ear

Dai

Pears

Smith

2018

2018 13th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2018)

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Abstract-Morphable models are useful shape priors for biometric recognition tasks. Here we present an iterative process of refinement for a 3D Morphable Model (3DMM) of the human ear that employs data augmentation. The process employs the following stages 1) landmark-based 3DMM fitting; 2) 3D template deformation to overcome noisy over-fitting; 3) 3D mesh editing, to improve the fit to manual 2D landmarks. These processes are wrapped in an iterative procedure that is able to bootstrap a weak, approximate model into a significantly better model. Evaluations using several performance metrics verify the improvement of our model using the proposed algorithm. We use this new 3DMM model-booting algorithm to generate a refined 3D morphable model of the human ear, and we make this new model and our augmented training dataset public.

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3D Modeling of craniofacial ontogeny and sexual dimorphism in children

Smith

Nashed²,

Duncan

et al. 2020

The Anatomical Record

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Background The range of normal variation of growth and development of the craniofacial region is of direct clinical interest but incompletely understood. Here we develop a statistical model of craniofacial growth and development to compare craniofacial ontogeny between age groups and sexes and pilot an approach to modeling that is relatively straightforward to apply in the context of clinical research and assessment. Methods The sample comprises head surface meshes captured using a 3dMD five‐camera system from 65 males and 47 females (range 3–20 years) from the Headspace project, Liverpool, UK. The surface meshes were parameterized using 16 anatomical landmarks and 59 semilandmarks on curves and surfaces. Modes and degrees of growth and development were assessed and compared among ages and sexes using Procrustes based geometric morphometric methods. Results Regression analyses indicate that 3–10 year olds undergo greater changes than 11–20 year olds and that craniofacial growth and development differs between these age groups. The analyses indicate that males extend growth allometrically into larger size ranges, contributing substantially to adult dimorphism. Comparisons of ontogenetic trajectories between sexes find no significant differences, yet when hypermorphosis is accounted for in the older age group there is a significant residual sexual dimorphism. Conclusions The study adds to knowledge of how adult craniofacial form and sexual dimorphism develop. It was carried out using readily available software which facilitates replication of this work in diverse populations to underpin clinical assessment of deformity and the outcomes of corrective interventions.

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Symmetric Shape Morphing for 3D Face and Head Modelling

Cited by 8 publications

References 33 publications

Statistical Modeling of Craniofacial Shape and Texture

Statistical Modeling of Craniofacial Shape and Texture

A Data-Augmented 3D Morphable Model of the Ear

3D Modeling of craniofacial ontogeny and sexual dimorphism in children

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