State-of-the-art face super-resolution methods employ deep convolutional
neural networks to learn a mapping between low- and high- resolution facial
patterns by exploring local appearance knowledge. However, most of these
methods do not well exploit facial structures and identity information, and
struggle to deal with facial images that exhibit large pose variations. In this
paper, we propose a novel face super-resolution method that explicitly
incorporates 3D facial priors which grasp the sharp facial structures. Our work
is the first to explore 3D morphable knowledge based on the fusion of
parametric descriptions of face attributes (e.g., identity, facial expression,
texture, illumination, and face pose). Furthermore, the priors can easily be
incorporated into any network and are extremely efficient in improving the
performance and accelerating the convergence speed. Firstly, a 3D face
rendering branch is set up to obtain 3D priors of salient facial structures and
identity knowledge. Secondly, the Spatial Attention Module is used to better
exploit this hierarchical information (i.e., intensity similarity, 3D facial
structure, and identity content) for the super-resolution problem. Extensive
experiments demonstrate that the proposed 3D priors achieve superior face
super-resolution results over the state-of-the-arts.

该研究提出了一种将 3D 人脸先验知识与深度卷积神经网络相结合的人脸超分辨率方法。实验结果表明，所提出的方法在面部超分辨率方面取得了优异的效果。

由三维面部先验引导的人脸超分辨率

Face Super-Resolution Guided by 3D Facial Priors

This paper presents a novel approach to synthesize automatically
age-progressed facial images in video sequences using Deep Reinforcement
Learning. The proposed method models facial structures and the longitudinal
face-aging process of given subjects coherently across video frames. The
approach is optimized using a long-term reward, Reinforcement Learning function
with deep feature extraction from Deep Convolutional Neural Network. Unlike
previous age-progression methods that are only able to synthesize an aged
likeness of a face from a single input image, the proposed approach is capable
of age-progressing facial likenesses in videos with consistently synthesized
facial features across frames. In addition, the deep reinforcement learning
method guarantees preservation of the visual identity of input faces after
age-progression. Results on videos of our new collected aging face AGFW-v2
database demonstrate the advantages of the proposed solution in terms of both
quality of age-progressed faces, temporal smoothness, and cross-age face
verification.

使用深度强化学习方法，该论文提出了一种新颖的自动合成视频序列中年龄递进的面部图像的方法，它能够将给定对象的面部结构和纵向面部衰老过程相互连贯地建模，并使用深度卷积神经网络中的深层特征提取进行优化，是其它先前面部衰老方法无法比拟的解决方案。

通过深度强化学习自动实现视频中的人脸老化

Automatic Face Aging in Videos via Deep Reinforcement Learning

Human faces are one interesting object class with numerous applications.
While significant progress has been made in the generic deblurring problem,
existing methods are less effective for blurry face images. The success of the
state-of-the-art image deblurring algorithms stems mainly from implicit or
explicit restoration of salient edges for kernel estimation. However, existing
methods are less effective as only few edges can be restored from blurry face
images for kernel estimation. In this paper, we address the problem of
deblurring face images by exploiting facial structures. We propose a deblurring
algorithm based on an exemplar dataset without using coarse-to-fine strategies
or heuristic edge selections. In addition, we develop a convolutional neural
network to restore sharp edges from blurry images for deblurring. Extensive
experiments against the state-of-the-art methods demonstrate the effectiveness
of the proposed algorithms for deblurring face images. In addition, we show the
proposed algorithms can be applied to image deblurring for other object
classes.

本文提出了一种基于样本数据集的去模糊算法以及卷积神经网络恢复模糊图像中的清晰边缘进行去模糊处理，实验表明该算法对于人脸图像去模糊非常有效，并且可以用于其他物体类别。

使用样本学习去模糊图像

Learning to Deblur Images with Exemplars

Face photo-sketch synthesis aims at generating a facial sketch/photo
conditioned on a given photo/sketch. It is of wide applications including
digital entertainment and law enforcement. Precisely depicting face
photos/sketches remains challenging due to the restrictions on structural
realism and textural consistency. While existing methods achieve compelling
results, they mostly yield blurred effects and great deformation over various
facial components, leading to the unrealistic feeling of synthesized images. To
tackle this challenge, in this work, we propose to use the facial composition
information to help the synthesis of face sketch/photo. Specially, we propose a
novel composition-aided generative adversarial network (CA-GAN) for face
photo-sketch synthesis. In CA-GAN, we utilize paired inputs including a face
photo/sketch and the corresponding pixel-wise face labels for generating a
sketch/photo. In addition, to focus training on hard-generated components and
delicate facial structures, we propose a compositional reconstruction loss.
Finally, we use stacked CA-GANs (SCA-GAN) to further rectify defects and add
compelling details. Experimental results show that our method is capable of
generating both visually comfortable and identity-preserving face
sketches/photos over a wide range of challenging data. Our method achieves the
state-of-the-art quality, reducing best previous Frechet Inception distance
(FID) by a large margin. Besides, we demonstrate that the proposed method is of
considerable generalization ability. We have made our code and results publicly
available: this https URL

本文提出一个基于面部构图信息的 Composition-aided Generative Adversarial Network (CA-GAN) 来生成面部素描 / 照片。通过使用成对的输入包括面部照片 / 素描和相应的面部标签来进行合成，最终使用堆叠的 CA-GANs (SCA-GAN) 去修正缺陷并增加有吸引力的细节，实验结果表明，我们的方法能够在广泛的具有挑战性的数据范围内生成视觉舒适、保持特征的面部素描 / 照片，并且取得了最佳的生成效果。