Existing methods of 3D dense face alignment mainly concentrate on accuracy,
thus limiting the scope of their practical applications. In this paper, we
propose a novel regression framework named 3DDFA-V2 which makes a balance among
speed, accuracy and stability. Firstly, on the basis of a lightweight backbone,
we propose a meta-joint optimization strategy to dynamically regress a small
set of 3DMM parameters, which greatly enhances speed and accuracy
simultaneously. To further improve the stability on videos, we present a
virtual synthesis method to transform one still image to a short-video which
incorporates in-plane and out-of-plane face moving. On the premise of high
accuracy and stability, 3DDFA-V2 runs at over 50fps on a single CPU core and
outperforms other state-of-the-art heavy models simultaneously. Experiments on
several challenging datasets validate the efficiency of our method. Pre-trained
models and code are available at this https URL

本文提出了一种名为 3DDFA-V2 的新颖回归框架，基于轻量级骨干和元联合优化策略，实现了速度、准确性和稳定性之间的平衡，并提出了虚拟合成方法，使得模型在高准确率和稳定性的前提下，可以在单个 CPU 核心上运行超过 50fps，并在几个具有挑战性的数据集上验证了该方法的有效性。

快速、准确和稳定的 3D 密集面部对齐

Towards Fast, Accurate and Stable 3D Dense Face Alignment

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。