  We propose a method for constructing generative models of 3D objects from a
single 3D mesh. Our method produces a 3D morphable model that represents shape
and albedo in terms of Gaussian processes. We define the shape deformations in
physical (3D) space and the albedo deformations as a combination of
physical-space and color-space deformations. 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 scan (one scan total, not one per
person), and further demonstrate how multiple scans can be incorporated to
improve performance without requiring dense correspondence. Our approach
enables the synthesis of 3D morphable models for 3D object categories where
dense correspondence between multiple scans is unavailable. We demonstrate this
by constructing additional 3D morphable models for fish and birds and use them
to perform simple inverse rendering tasks. We share the code used to generate
these models and to perform our inverse rendering and registration experiments.
