This paper investigates how far a very deep neural network is from attaining
close to saturating performance on existing 2D and 3D face alignment datasets.
To this end, we make the following 5 contributions: (a) we construct, for the
first time, a very strong baseline by combining a state-of-the-art architecture
for landmark localization with a state-of-the-art residual block, train it on a
very large yet synthetically expanded 2D facial landmark dataset and finally
evaluate it on all other 2D facial landmark datasets. (b) We create a guided by
2D landmarks network which converts 2D landmark annotations to 3D and unifies
all existing datasets, leading to the creation of LS3D-W, the largest and most
challenging 3D facial landmark dataset to date ~230,000 images. (c) Following
that, we train a neural network for 3D face alignment and evaluate it on the
newly introduced LS3D-W. (d) We further look into the effect of all
"traditional" factors affecting face alignment performance like large pose,
initialization and resolution, and introduce a "new" one, namely the size of
the network. (e) We show that both 2D and 3D face alignment networks achieve
performance of remarkable accuracy which is probably close to saturating the
datasets used. Training and testing code as well as the dataset can be
downloaded from this https URL

本文研究了非常深层神经网络在现有的 2D 和 3D 人脸对齐数据集中实现饱和性能的距离。通过构建一个强大的基线，创建引导 2D 标记的网络并创建最大的和最具挑战性的 3D 人脸标记数据集 LS3D-W，训练和评估神经网络，并探究大小等因素的影响，该研究证明 2D 和 3D 人脸对齐网络均实现了接近饱和性能的显著高精度水平。