  Three-dimensional Morphable Models (3DMMs) are powerful statistical tools for
representing the 3D surfaces of an object class. In this context, we identify
an interesting question that has previously not received research attention: is
it possible to combine two or more 3DMMs that (a) are built using different
templates that perhaps only partly overlap, (b) have different representation
capabilities and (c) are built from different datasets that may not be
publicly-available? In answering this question, we make two contributions.
First, we propose two methods for solving this problem: i. use a regressor to
complete missing parts of one model using the other, ii. use the Gaussian
Process framework to blend covariance matrices from multiple models. Second, as
an example application of our approach, we build a new face-and-head shape
model that combines the variability and facial detail of the LSFM with the full
head modelling of the LYHM. The resulting combined shape model achieves
state-of-the-art performance and outperforms existing head models by a large
margin. Finally, as an application experiment, we reconstruct full head
representations from single, unconstrained images by utilizing our proposed
large-scale model in conjunction with the FaceWarehouse blendshapes for
handling expressions.
