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 publiclyavailable?
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 Face-
Warehouse blendshapes for handling expressions.