  Most 3D face reconstruction methods rely on 3D morphable models, which
disentangle the space of facial deformations into identity geometry,
expressions and skin reflectance. These models are typically learned from a
limited number of 3D scans and thus do not generalize well across different
identities and expressions. We present the first approach to learn complete 3D
models of face identity geometry, albedo and expression just from images and
videos. The virtually endless collection of such data, in combination with our
self-supervised learning-based approach allows for learning face models that
generalize beyond the span of existing approaches. Our network design and loss
functions ensure a disentangled parameterization of not only identity and
albedo, but also, for the first time, an expression basis. Our method also
allows for in-the-wild monocular reconstruction at test time. We show that our
learned models better generalize and lead to higher quality image-based
reconstructions than existing approaches.
