Review of statistical shape spaces for 3D data with comparative analysis for human faces

By Alan Brunton, Augusto Salazar, Timo Bolkart, Stefanie Wuhrer. 2012

82 citations recorded by the source corpus.

Abstract With systems for acquiring 3D surface data being evermore commonplace, it has become important to reliably extract specific shapes from the acquired data. In the presence of noise and occlusions, this can be done through the use of statistical shape models, which are learned from databases of clean examples of the shape in question. In this paper, we review, analyze and compare different statistical models: from those that analyze the variation in geometry globally to those that analyze the variation in geometry locally. We first review how different types of models have been used in the literature, then proceed to define the models and analyze them theoretically, in terms of both their statistical and computational aspects. We then perform extensive experimental comparison on the task of model fitting, and give intuition about which type of model is better for a few applications. Due to the wide availability of databases of high-quality data, we use the human face as the specific shape we wish to extract from corrupted data.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Geometry and Topology

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Citations in the corpus

Generating 3D faces using Convolutional Mesh Autoencoders (2018, background): “[13] for a comprehensive overview of 3D face representations.”
A 3D Morphable Model Learnt from 10,000 Faces (2016, methodology): “Similarly to [14, 32], a simple model fitting is employed, which consists of (1) searching for the nearest vertex in the test mesh to establish correspondences between that mesh and the model, (2) projecting the test mesh onto the model”
3D Morphable Face Models—Past, Present, and Future (2019, unknown): “To allow modeling localized variations, the multilinear model has been applied to wavelet coefficients at different levels of detail [Brunton et al. 2014a].”
Large Scale 3D Morphable Models (2017, methodology): “…available Morphable Models of human faces in neutral expression, namely the BFM model (Paysan et al. 2009a, Paysan et al. 2009b), the PCA model of Brunton et al. (2014b), Bolkart et al. (2013), which will be hereafter referred to as Brunton et al. model , and the Surrey face model (Huber et al.…”