Face alignment, which fits a face model to an image and extracts the semantic
meanings of facial pixels, has been an important topic in CV community.
However, most algorithms are designed for faces in small to medium poses (below
45 degree), lacking the ability to align faces in large poses up to 90 degree.
The challenges are three-fold: Firstly, the commonly used landmark-based face
model assumes that all the landmarks are visible and is therefore not suitable
for profile views. Secondly, the face appearance varies more dramatically
across large poses, ranging from frontal view to profile view. Thirdly,
labelling landmarks in large poses is extremely challenging since the invisible
landmarks have to be guessed. In this paper, we propose a solution to the three
problems in an new alignment framework, called 3D Dense Face Alignment (3DDFA),
in which a dense 3D face model is fitted to the image via convolutional neutral
network (CNN). We also propose a method to synthesize large-scale training
samples in profile views to solve the third problem of data labelling.
Experiments on the challenging AFLW database show that our approach achieves
significant improvements over state-of-the-art methods.

提出了一种新的 3D 密集面部对齐框架 (3DDFA)，通过卷积神经网络 (CNN) 将密集的 3D 面部模型拟合到图像中，解决了面部对齐中的圆形视图问题，面部外观变化剧烈问题和面部关键点标记困难问题，并通过在 profile 视图下合成大规模训练样本来解决关键点标记困难问题，并在具有挑战性的 AFLW 数据库上实验，证明我们的方法明显优于现有最先进方法。