  We present a self-supervised learning approach to learning monocular 3D face
reconstruction with a pose guidance network (PGN). First, we unveil the
bottleneck of pose estimation in prior parametric 3D face learning methods, and
propose to utilize 3D face landmarks for estimating pose parameters. With our
specially designed PGN, our model can learn from both faces with fully labeled
3D landmarks and unlimited unlabeled in-the-wild face images. Our network is
further augmented with a self-supervised learning scheme, which exploits face
geometry information embedded in multiple frames of the same person, to
alleviate the ill-posed nature of regressing 3D face geometry from a single
image. These three insights yield a single approach that combines the
complementary strengths of parametric model learning and data-driven learning
techniques. We conduct a rigorous evaluation on the challenging AFLW2000-3D,
Florence and FaceWarehouse datasets, and show that our method outperforms the
state-of-the-art for all metrics.
