Face alignment, which fits a face model to an image and extracts the semantic meanings of facial pixels, has been an important topic in the computer vision community. However, most algorithms are designed for faces in small to medium poses (yaw angle is smaller than 45 degrees), which lack the ability to align faces in large poses up to 90 degrees. The challenges are three-fold. Firstly, the commonly used landmark face model assumes that all the landmarks are visible and is therefore not suitable for large poses. Secondly, the face appearance varies more drastically across large poses, from the frontal view to the profile view. Thirdly, labelling landmarks in large poses is extremely challenging since the invisible landmarks have to be guessed. In this paper, we propose to tackle these three challenges in an new alignment framework termed 3D Dense Face Alignment (3DDFA), in which a dense 3D Morphable Model (3DMM) is fitted to the image via Cascaded Convolutional Neural Networks. We also utilize 3D information to synthesize face images in profile views to provide abundant samples for training. Experiments on the challenging AFLW database show that the proposed approach achieves significant improvements over the state-of-the-art methods.

本文提出了一种新的面部对齐框架，称为3D Dense Face Alignment (3DDFA)，其中使用级联卷积神经网络将密集3D可变形模型（3DMM）配合到图像中。本文还利用3D信息合成正面视图和侧面视图的面部图像以提供丰富的样本进行训练。在具有挑战性的AFLW数据库上的实验表明，所提出的方法比现有技术取得了显着的进步。

全姿态范围下的人脸对齐: 三维全方位解决方案