  Face alignment is a classic problem in the computer vision field. Previous
works mostly focus on sparse alignment with a limited number of facial landmark
points, i.e., facial landmark detection. In this paper, for the first time, we
aim at providing a very dense 3D alignment for large-pose face images. To
achieve this, we train a CNN to estimate the 3D face shape, which not only
aligns limited facial landmarks but also fits face contours and SIFT feature
points. Moreover, we also address the bottleneck of training CNN with multiple
datasets, due to different landmark markups on different datasets, such as 5,
34, 68. Experimental results show our method not only provides high-quality,
dense 3D face fitting but also outperforms the state-of-the-art facial landmark
detection methods on the challenging datasets. Our model can run at real time
during testing.
