  3D face reconstruction from a single image is a challenging problem,
especially under partial occlusions and extreme poses. This is because the
uncertainty of the estimated 2D landmarks will affect the quality of face
reconstruction. In this paper, we propose a novel joint 2D and 3D optimization
method to adaptively reconstruct 3D face shapes from a single image, which
combines the depths of 3D landmarks to solve the uncertain detections of
invisible landmarks. The strategy of our method involves two aspects: a
coarse-to-fine pose estimation using both 2D and 3D landmarks, and an adaptive
2D and 3D re-weighting based on the refined pose parameter to recover accurate
3D faces. Experimental results on multiple datasets demonstrate that our method
can generate high-quality reconstruction from a single color image and is
robust for self-occlusion and large poses.
