  We propose a method for constructing generative models of 3D objects from a
single 3D mesh and improving them through unsupervised low-shot learning from
2D images. Our method produces a 3D morphable model that represents shape and
albedo in terms of Gaussian processes. Whereas previous approaches have
typically built 3D morphable models from multiple high-quality 3D scans through
principal component analysis, we build 3D morphable models from a single scan
or template. As we demonstrate in the face domain, these models can be used to
infer 3D reconstructions from 2D data (inverse graphics) or 3D data
(registration). Specifically, we show that our approach can be used to perform
face recognition using only a single 3D template (one scan total, not one per
person). We extend our model to a preliminary unsupervised learning framework
that enables the learning of the distribution of 3D faces using one 3D template
and a small number of 2D images. This approach could also provide a model for
the origins of face perception in human infants, who appear to start with an
innate face template and subsequently develop a flexible system for perceiving
the 3D structure of any novel face from experience with only 2D images of a
relatively small number of familiar faces.
