  Three-dimensional face dense alignment and reconstruction in the wild is a
challenging problem as partial facial information is commonly missing in
occluded and large pose face images. Large head pose variations also increase
the solution space and make the modeling more difficult. Our key idea is to
model occlusion and pose to decompose this challenging task into several
relatively more manageable subtasks. To this end, we propose an end-to-end
framework, termed as Self-aligned Dual face Regression Network (SADRNet), which
predicts a pose-dependent face, a pose-independent face. They are combined by
an occlusion-aware self-alignment to generate the final 3D face. Extensive
experiments on two popular benchmarks, AFLW2000-3D and Florence, demonstrate
that the proposed method achieves significant superior performance over
existing state-of-the-art methods.
