3D face alignment is a very challenging and fundamental problem in computer
vision. Existing deep learning-based methods manually design different networks
to regress either parameters of a 3D face model or 3D positions of face
vertices. However, designing such networks relies on expert knowledge, and
these methods often struggle to produce consistent results across various face
poses. To address this limitation, we employ Neural Architecture Search (NAS)
to automatically discover the optimal architecture for 3D face alignment. We
propose a novel Multi-path One-shot Neural Architecture Search (MONAS)
framework that leverages multi-scale features and contextual information to
enhance face alignment across various poses. The MONAS comprises two key
algorithms: Multi-path Networks Unbiased Sampling Based Training and Simulated
Annealing based Multi-path One-shot Search. Experimental results on three
popular benchmarks demonstrate the superior performance of the MONAS for both
sparse alignment and dense alignment.

通过神经架构搜索的多路径单次搜索算法，提出了一种新的提高不同面部姿势下人脸对齐准确性的方法。实验结果表明，该方法在稀疏对齐和密集对齐方面均具有卓越性能。

多路径神经架构搜索的稳健三维人脸对齐

Robust 3D Face Alignment with Multi-Path Neural Architecture Search

Facial landmark localisation in images captured in-the-wild is an important
and challenging problem. The current state-of-the-art revolves around certain
kinds of Deep Convolutional Neural Networks (DCNNs) such as stacked U-Nets and
Hourglass networks. In this work, we innovatively propose stacked dense U-Nets
for this task. We design a novel scale aggregation network topology structure
and a channel aggregation building block to improve the model's capacity
without sacrificing the computational complexity and model size. With the
assistance of deformable convolutions inside the stacked dense U-Nets and
coherent loss for outside data transformation, our model obtains the ability to
be spatially invariant to arbitrary input face images. Extensive experiments on
many in-the-wild datasets, validate the robustness of the proposed method under
extreme poses, exaggerated expressions and heavy occlusions. Finally, we show
that accurate 3D face alignment can assist pose-invariant face recognition
where we achieve a new state-of-the-art accuracy on CFP-FP.

我们提出了一种基于堆叠密集 U-Nets 的人脸关键点检测模型，采用新颖的网络结构和协同损失函数，通过变形卷积实现空间不变性，并在多个真实场景数据集上验证了模型的鲁棒性和在 3D 人脸识别方面的应用。

基于双变形器的堆叠稠密 U-Net 网络用于鲁棒性人脸对齐

Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment

Recently proposed robust 3D face alignment methods establish either dense or
sparse correspondence between a 3D face model and a 2D facial image. The use of
these methods presents new challenges as well as opportunities for facial
texture analysis. In particular, by sampling the image using the fitted model,
a facial UV can be created. Unfortunately, due to self-occlusion, such a UV map
is always incomplete. In this paper, we propose a framework for training Deep
Convolutional Neural Network (DCNN) to complete the facial UV map extracted
from in-the-wild images. To this end, we first gather complete UV maps by
fitting a 3D Morphable Model (3DMM) to various multiview image and video
datasets, as well as leveraging on a new 3D dataset with over 3,000 identities.
Second, we devise a meticulously designed architecture that combines local and
global adversarial DCNNs to learn an identity-preserving facial UV completion
model. We demonstrate that by attaching the completed UV to the fitted mesh and
generating instances of arbitrary poses, we can increase pose variations for
training deep face recognition/verification models, and minimise pose
discrepancy during testing, which lead to better performance. Experiments on
both controlled and in-the-wild UV datasets prove the effectiveness of our
adversarial UV completion model. We achieve state-of-the-art verification
accuracy, $94.05\%$, under the CFP frontal-profile protocol only by combining
pose augmentation during training and pose discrepancy reduction during
testing. We will release the first in-the-wild UV dataset (we refer as WildUV)
that comprises of complete facial UV maps from 1,892 identities for research
purposes.

该论文提出了一种使用深度卷积神经网络训练面部 UV 图完成模型的框架，从而实现更好的面孔识别表现。该方法通过附加完成的 UV 图到安装的网格并生成任意姿态的实例，增加了姿态变化以训练深度面部识别 / 验证模型，并在测试期间最小化姿态差异，证明了对于有完整 UV map 数据集的人脸库，其具有极高的人脸识别准确率。

UV-GAN: 针对位姿不变人脸识别的对抗性人脸 UV 图像补全

UV-GAN: Adversarial Facial UV Map Completion for Pose-invariant Face  Recognition

This paper describes our submission to the 1st 3D Face Alignment in the Wild
(3DFAW) Challenge. Our method builds upon the idea of convolutional part
heatmap regression [1], extending it for 3D face alignment. Our method
decomposes the problem into two parts: (a) X,Y (2D) estimation and (b) Z
(depth) estimation. At the first stage, our method estimates the X,Y
coordinates of the facial landmarks by producing a set of 2D heatmaps, one for
each landmark, using convolutional part heatmap regression. Then, these
heatmaps, alongside the input RGB image, are used as input to a very deep
subnetwork trained via residual learning for regressing the Z coordinate. Our
method ranked 1st in the 3DFAW Challenge, surpassing the second best result by
more than 22%.

该论文描述了一种基于卷积部位热图回归的方法，将其用于 3D 人脸对齐并获得了 3DFAW 挑战赛的第一名，超过第二名超过 22% 的结果。