  We present a conditional estimation (CEST) framework to learn 3D facial
parameters from 2D single-view images by self-supervised training from videos.
CEST is based on the process of analysis by synthesis, where the 3D facial
parameters (shape, reflectance, viewpoint, and illumination) are estimated from
the face image, and then recombined to reconstruct the 2D face image. In order
to learn semantically meaningful 3D facial parameters without explicit access
to their labels, CEST couples the estimation of different 3D facial parameters
by taking their statistical dependency into account. Specifically, the
estimation of any 3D facial parameter is not only conditioned on the given
image, but also on the facial parameters that have already been derived.
Moreover, the reflectance symmetry and consistency among the video frames are
adopted to improve the disentanglement of facial parameters. Together with a
novel strategy for incorporating the reflectance symmetry and consistency, CEST
can be efficiently trained with in-the-wild video clips. Both qualitative and
quantitative experiments demonstrate the effectiveness of CEST.
