are continuous and attribute regression loss is used instead. This loss
function is formulated as:

$\displaystyle\mathcal{L}_{cls}=$ $\displaystyle\mathbb{E}_{x^{\hat{b}}\sim
p_{g},b\sim p_{attr}}[-logD_{cls}(b|x^{\hat{b}})]$ (3)
$\displaystyle+\mathbb{E}_{x^{b}\sim p_{data}}[-logD_{cls}(b|x^{b})]$

where $D_{cls}$ demonstrates the classification branch of the discriminator.
The first term of the above loss function calculates the classification error
for generated images and is used for training the generator and the second
term gives the error over real data and is only used for training the
attribute classifier itself.

Reconstruction loss: When the input image is edited based on its original
attribute vector $(a)$, the generated output image $(x^{\hat{a}})$ is expected
to be the same as the input image $(x^{a})$. This constraint guarantees that
excluded attributes will remain untouched during the editing process. This
objective function is formulated as:

$\displaystyle\mathcal{L}_{\text{rec}}=\mathbb{E}_{x^{a}\sim
p_{data},x^{\hat{a}}=G(x^{a},a)}\bigg{[}\left\|x^{a}-x^{\hat{a}}\right\|_{1}\bigg{]}$
(4)

Consistency loss: consistency loss, also called recovery loss or identity
loss, evaluates how similar are $G(G(x^{a};b);a)$ and the input image $x^{a}$.
It is defined as:

$\displaystyle\mathcal{L}_{consistency}=\mathbb{E}_{x^{a}\sim p_{data},b\sim
p_{attr}}\bigg{[}\left\|G\left(G\left(x^{a},b\right),a\right)-x^{a}\right\|^{2}\bigg{]}$
(5)

Perceptual loss: To check the content similarity of the input image and the
edited image in the semantic level, perceptual loss penalizes the difference
between features of the two images extracted by a pre-trained feature
extractor network, e.g. VGG19.

Attention loss: In mask-guided approach, as stated before, an attention mask
$A$ is responsible for specifying the regions which need modifications.
However, the attention mask can easily saturate to 1 and lose its effect. So,
the attention loss is introduced to regularize the mask and make it as sparse
as possible to prevent saturation. It is defined as L1 or L2 norm of the
generated mask.

Total variation loss: Total variation (TV) loss is not a common loss function
among the facial attribute editing models. However, in some models, this loss
function has been used to encourage models to generate spatially smooth
images.

Other losses: Some works have also proposed their own new loss functions to
make the training procedure more stable or to generate output images with
higher quality. For example, Kim et al. [78] introduce CAM-consistency loss
which overcomes cycle-consistency loss’s weakness and can be applied to all
editing models. The cycle-consistency loss cannot preserve details in regions
that are not relevant to the target attributes. While the input and the
regenerated images have differences and cannot be compared directly, CAM-
consistency only considers attribute-irrelevant parts of two images in the
comparison. To this end, an auxiliary classifier attends to the model to
determine the regions of interest. Grad-CAM is a visualization method which
specifies where on an image a classifier attends more regarding a specific
class. The output of Grad-CAM can be used as a mask of attribute relevant
parts on the input image. The CAM-consistency loss is formulated as:

$\mathcal{L}_{CAM}=\mathbb{E}_{x^{a}\sim
p_{data},x^{\hat{b}}=G(x^{a},b)}\bigg{[}1-M_{CAM}\odot||x^{a}-\hat{x}^{b}||_{1}\bigg{]}$
(6)

where $M_{CAM}$ is the attention mask generated by Grad-CAM.

##  IV Datasets and Evaluation Metrics

###  IV-A Datasets

Table II: The list of the datasets related to facial attribute editing tasks.

No | Dataset | #Features | Year | #Samples | #Identities | Uncontrolled/ Controlled Environment | Annotation
---|---|---|---|---|---|---|---
1 | CAS-PEAL [43] | 3 | 2003 | 99594 | 1040 | Controlled environment | Pose, Expression
2 | MORPH [131] | 1 | 2006 | 55134 | 13617 | Controlled environment | Age
3 | LFW [63] | 1 | 2007 | 13233 | 5749 | Uncontrolled environment | Identity
4 | FaceTracer [82] | 10 | 2008 | 15000 | 15000 | Uncontrolled environment | Facial attribute, Expression
5 | PubFig [83] | 73 | 2009 | 58797 | 200 | Uncontrolled environment | _
6 | MultiPIE [47] | 2 | 2010 |  $\sim$750000 | 337 | Controlled environment | Pose, Expression
7 | RaFD [86] | 3 | 2010 | 8040 | 67 | Controlled environment | Expression, Pose, Gaze direction
8 | FaceWarehouse [11] | 1 | 2013 | 3000 | 150 | Controlled environment | 3D Expression
9 | CACD [15] | 1 | 2014 | 163446 | 2000 | Uncontrolled environment | Identity
10 | FGNET [42] | 1 | 2014 | 1002 | 82 | Uncontrolled environment | Age
11 | CelebA [108] | 40 | 2015 | 202599 | 10177 | Uncontrolled environment | 40 facial attributes+ 5 landmarks
12 | IMDB-WIKI[135] | 2 | 2015 | 523051 | 20284 | Uncontrolled environment | Age, Gender, Identity
13 | IJB-A [80] | 1 | 2015 | 5712 | 500 | Uncontrolled environment | Identity
14 | CFP [138] | 1 | 2016 | 7000 | 500 | Uncontrolled environment | Identity, Landmarks
15 | celebA-HQ [75] | 40 | 2017 | 30000 | 30000 | Uncontrolled enviroment | 40 facial attributes+ 5 landmarks
16 | PPB [9] | 3 | 2017 | 1270 | 1270 | Controlled environment | Gender, Age
17 | Face aging [103] | 1 | 2017 | 15030 | 15030 | Uncontrolled environment | Age
18 | UTKFace [211] | 3 | 2017 |  $\sim$20000 | 20000 | Uncontrolled environment | Age, Gender, 68 Landmarks
19 | AgeDB[114] | 1 | 2017 | 16488 | 568 | Uncontrolled environment | Age
20 | CLF [30] | 1 | 2018 | 3682 | 919 | Uncontrolled environment | Age
21 | WFLW [178] | _ | 2018 | 10000 | 10000 | Uncontrolled environment | 98 Landmarks
22 | IMDB-Face [165] | 1 | 2018 | 1700000 | 59000 | Uncontrolled environment | Identity
23 | FairFace [74] | 3 | 2019 | 108501 | 108501 | Uncontrolled environment | Race, Gender, Age
24 | MVF-HQ [41] | 3 | 2019 | 120283 | 479 | Controlled environment | Pose, Expression
25 | CelebAMask-HQ [88] | 19 | 2020 | 30000 | 30000 | Uncontrolled environment | Facial component masks
26 | MAAD-Face [151] | 47 | 2020 | 3300000 | 9100 | Uncontrolled environment | 47 Binary facial attributes

Numerous datasets have been proposed for face-related researches in the past
decades. In this section, we review the datasets with facial attribute
annotations that can be used in facial attribute editing tasks although most
of them have been initially proposed for other tasks like face recognition or
facial attribute classification. Based on the type of their annotations, the
related datasets can be categorized into four main groups: multi-attribute,
identity, age, and pose-expression. In the datasets of the first group, images
have been labeled with many facial attributes and have been mainly used in
facial attribute classification and editing models. In the identity datasets,
there are a number of distinct identities and several face images of each
identity which makes them suitable for face recognition and verification
tasks. Datasets in the third group have been annotated with the age of the
individuals and therefore are appropriate for developing age-invariant face
recognition/verification systems and face aging models. The last group of the
face datasets is dedicated to pose and expression features usually recorded
under laboratory conditions and can be used for developing face reenactment
models. In the following, a number of datasets in each category are reviewed.
The datasets are also summarized in Table II.

Multi-attribute: CelebA [108] is a well-known dataset that has provided a
diverse set of annotations for all of its 202599 images. The annotations
include 40 binary attributes such as _gender_ , _wavy hair_ , _eyeglasses_ ,
_bangs_ , _oval face_ , _smiling_ , _mustache_ , and a lot of other binary
attributes. In addition to these 40 attributes, each image in CelebA dataset
has 5 landmark locations. CelebA is useful for training facial attribute
classifiers. CelebA-HQ [75] dataset is a high-quality version of the CelebA
which has 30000 images each upsampled from an image of CelebA. Similar to
CelebA, all images in CeleA-HQ have 5 landmark points along with 40 attributes
annotations. CelebAMask-HQ [88] dataset is a version of CelebA-HQ which
provides a mask photo for each image in CelebA-HQ dataset. The mask image
locates 19 facial components, such as eyes, eyebrows, nose, skin, hair, hat,
eyeglasses, ears, mouth, lip, earrings, necklace, cloth, and neck. PubFig
[83], Helen [87], and FaceTracer [82] are other datasets that cover multi-
attributes.

Identity: LFW (Labeled Face in the Wild) dataset [63] is a quite popular
dataset containing 13,233 images of 5,749 individuals, of which 1,680
identities have two or more images. All images have been collected from the
web and have been annotated with people’s names. LFW is widely used in
unconstrained face recognition problems. IMDB-Face [165] is a more recent
large-scale noise-controlled dataset that contains 1.7M face images of 5.9K
identities that are captured from the IMDb website. IJB-A [80] dataset
consists of 5,712 images and 2,085 videos from 500 identities and has been
designed for a specific face recognition challenge. CFP (Celebrities in
Frontal-Profile) [138] is another dataset of this category that comprises 7000
face images from 500 identities.

Age: All datasets of this category provide the age label for their face
images. Morph [131] contains 55,134 photos of 13,617 individuals between 16 to
77 years old and is a suitable dataset for facial age estimation tasks. Face
Aging [103] is another similar dataset in which 15,030 images are divided into
7 age groups including 0-10, 11-18, 19-30, 31-40, 41-50, 51-60 and 60+. The
ages of the face images of FGNET [42] vary from 0 to 69. AgeDB [114] dataset
contains 16488 photos of 568 subjects that are 0 to 100 years old and are
divided into 10 groups. CACD (Cross-Age Celebrity Dataset) [15] is a
collection of 163,446 images from 2,000 celebrities. Each image is annotated
with the year which the photo was taken (2004-2013). CLF (Children
Longitudinal Face) [30] is devoted to age groups between 2 to 18. CALFW
(Cross-Age LFW) dataset [212] covers ages between 0 to 60. FairFace [74], PPB
[9], UTKFace [211], and IMDB-WIKI [135] are other qualified datasets for
facial age-related tasks.

Pose and Expression: The focus of this group of datasets is on the pose and
expression of the face. MultiPIE (Multi Pose, Illumination, Expressions) [47]
dataset’s images have been recorded under certain conditions with 19
flashlights in different locations. More than 750,000 images of 337 subjects
in this dataset cover various poses and expressions. RaFD (Radboud Faces
Database) [86] encompass 8,040 face images of 67 subjects which display 8
different expressions in three different gaze directions and five poses. MVF-
HQ [41] covers 13 poses and three different expressions and has been recorded
in the laboratory conditions. FaceWarehouse [11] is a 3D dataset, in which all
images have been labeled by one of 20 expressions. CFEE[36] dataset has
divided its face images into 22 expression groups including _disgusted_ ,
_happily surprised_ , _happily disgusted_ , _sadly fearful_ , _sadly angry_ ,
and _sadly surprised_. EmotioNet [38] dataset has also focused on expression
attribute. The main difference between the EmotioNet dataset and all previous
expression datasets is that the images of EmotioNet dataset are not taken in
certain conditions or a laboratory.

###  IV-B Evaluation Metrics

The evaluation metrics employed for assessing the goodness of the proposed
facial image manipulation methods can be divided into qualitative and
quantitative measures. The qualitative evaluations are mainly based on the
human appraisal of the manipulated images while the quantitative methods
assign a numerical score to each generated image which expresses the quality
of the image itself or the edit operation performed on the image. In the
following, the main evaluation metrics of each category are presented.

####  IV-B1 Qualitative Analysis

As the output of facial image editing is generally produced for human
audiences, qualitative evaluations and comparisons are quite prevalent in
facial image editing literature. Two main methods of this category of metrics
are visual analysis and user study. Almost all of the studied models report
sample outputs of their proposed models and visually compare them with the
results of other methods or report the results of some user studies on these
photos.

Visual Analysis: In this evaluation method, sample outputs of the proposed
models are presented and general quality aspects of the images or specific
target attributes are investigated. The presence of artifacts,
blurriness/sharpness of the image, and having natural skin color and texture
are some general factors usually considered in the visual analysis of the
manipulated images regardless of the target of manipulation. However, an
important part of visual evaluations is based on the goal of the image
manipulation task. As mentioned earlier, the goal of these models is to change
the value of categorical attributes or to control the intensity of continuous
attributes [144]. The outputs of the facial attribute editing models should
have the target attribute values, e.g. smiling, while the non-target
attributes remain unchanged compared to the input image which shows the
disentanglement of the attributes in the proposed models [91]. The aim of the
visual analysis is to check these properties of the reported results. While
the visual analysis of the results is essential in this field, it is not
sufficient as the evaluation is limited to a small subset of manipulated
images.

User Study: In user studies, a relatively large group of people are asked to
give their opinions about the quality of the outputs of a model or to compare
the results of two or more models. The aggregated outcomes of the study are
then reported as an evaluation metric. In a simple scenario, the results of
the models on a number of images are shown to the participants to select the
best results for each image. The number of images for which a model is
selected as the best performing model is used as a measure of the goodness of
that model. To prepare a user study, one has to decide on the methods to be
compared, the test images, how the outputs are going to be evaluated or
compared (e.g. image quality, identity preservation, or naturalness), the
group of evaluators, and the statistical measures that are going to be
calculated from the users’ opinions. There are some tools to perform these
user studies, such as Amazon Mechanical Turk (AMT) and Google Form, while it
also possible to use self-produced applications. Most of the works studied in
this survey have conducted some sort of user studies to compare their results
with other models from human points of view (e.g. [123, 44, 148, 81, 14, 94,
100, 95, 216, 51]).

It should be noted that the user studies can be considered either qualitative
or quantitative evaluation metric as on one hand, the quality of the generated
images is evaluated based on human judgments (as in the qualitative measures),
and on the other hand, some result is reported as numerical scores (as in the
quantitative measures). The lack of other appropriate quantitative evaluation
metrics has encouraged the use of user studies as a quantitative metric in
many researches.

If the test images, the evaluating population, and the polling strategies are
set up carefully, the results of the user studies are more reliable than the
visual analysis. The set of test images should be diverse enough to properly
cover different challenges of the related task. The evaluating groups should
not be biased towards a special model and must answer the questions carefully.
Finally, the questionnaire should be devised carefully; for example, it is a
good practice to shuffle the results of different models for each test image
so that the answerers are not biased toward a special choice through the
evaluation process. However, visual analysis provides a more tangible
understanding of the quality of the edited images and in practice it is better
to how both visual analysis and user studies in your work.

####  IV-B2 Quantitative Analysis

There are some quantitative metrics for assessing the performance of various
facial attribute editing models. These measures assign numerical scores to
each manipulated image based on a mathematical formula. Compared to human-
based qualitative evaluations, quantitative measures are reproducible and are
not subject to human biases. However, it is difficult to have perfect
quantitative measures for some desired qualities.

The quantitative metrics can be divided into two main groups. The first group
includes image quality assessment metrics trying to measure the quality of a
resulting face image independent of the requested edits while the second group
investigates a pair of input and output face images to analyze the performance
of the model with respect to the expected modifications.

1) Image Quality Assessment (IQA): This group of metrics aims to quantize the
quality of the edited images. Two main types of IQA metrics are non-reference
and full-reference quality metrics. In non-reference IQA, the evaluation
algorithm just gets one input (generated image) and estimates the quality of
the image, while in full-reference IQA, the resulting image is compared with a
ground-truth and a score is assigned according to the similarity of the
resulting image to the expected target image. While these two types of quality
measures are usually calculated by predefined formulas, some neural networks
have been proposed for this purpose where the metric can be defined by a set
of labeled images. All of these evaluation metrics and methods are studied in
the following.

Non-reference IQA:

  * •

Inception Score (IS) has been introduced by Salimans et al. [136] for
evaluating the quality of synthesized images by GANs. IS is usually used in
training a GAN or an image-to-image translation model to guide the models to
generate high-quality images [203]. A modified version of IS is Conditional
Inception Score (CIS) [66] which is more applicable in multi-modal image
translation works. Although both IS and CIS seek to capture the high quality
and diversity of real data, they are not usually used in evaluating facial
attribute editing models. A drawback of IS is that it does not compare the
statistics of real-world samples with those of the synthetic samples which has
been solved in Frechet Inception Distance (FID)score.

  * •

Frechet Inception Distance (FID) score has been introduced by [59] and is a
standard metric for evaluating the quality of generated images (here, edited
images) by GANs. FID score is an improved version of the inception score and
demonstrates the distance between the distribution of the synthesized images
and that of the real images. FID, also known as Wasserstein-2 distance, takes
its name from the Inception model and Frechet Distance. At first, feature
vectors of two datasets (1024 real images and 1024 synthesized images) are
extracted using the inception network. Then, the means and covariances of
these two datasets are calculated. Finally, FID is calculated as the Frechet
distance of two sets of feature vectors’ distributions. FID is formulated as
equation 7 and its lower values demonstrate the better quality of manipulated
images. It is notable that experiments have shown that FID values are
generally consistent with human judgment [59]. Many studies have utilized FID
score to evaluate the quality of the manipulated face images, such as [203,
198, 51, 216, 3, 27, 37, 28, 161, 81, 148, 91]. If the feature vectors of the
images generated by the GAN model have multinomial Gaussian distribution
$N(\boldsymbol{m},\boldsymbol{C})$ and the features of the real world images
have $N(\boldsymbol{m}_{w},\boldsymbol{C}_{w})$, FID score is defined as:

$\displaystyle
d^{2}\left((\boldsymbol{m},\boldsymbol{C}),\left(\boldsymbol{m}_{w},\boldsymbol{C}_{w}\right)\right)$
$\displaystyle=\left\|\boldsymbol{m}-\boldsymbol{m}_{w}\right\|_{2}^{2}$ (7)
$\displaystyle+\operatorname{Tr}\left(\boldsymbol{C}+\boldsymbol{C}_{w}-2\left(\boldsymbol{CC}_{w}\right)^{1/2}\right)$

Full-Reference IQA: The structural similarity (SSIM) index, peak signal-to-
noise ratio (PSNR), and learned perceptual image patch similarity (LPIPS) are
some of the most common full-reference IQA metrics utilized in most papers. In
the following, these measures are described in more details.

  * •

SSIM [172] is based on the human visual system which is adapted for obtaining
structural information from the scene. SSIM can provide an approximation to
image quality perceived by human. This metric is based on the concepts of
local luminance, contrast, and structure in two images. If $x$ and $y$ are two
non-negative images, the luminance, contrast, and structure concepts are
defined in equations 8, 9 and 10, respectively.

$l(\mathbf{x},\mathbf{y})=\frac{2\mu_{x}\mu_{y}+C_{1}}{\mu_{x}^{2}+\mu_{y}^{2}+C_{1}}$
(8)

$c(\mathbf{x},\mathbf{y})=\frac{2\sigma_{x}\sigma_{y}+C_{2}}{\sigma_{x}^{2}+\sigma_{y}^{2}+C_{2}}$
(9)

$s(\mathbf{x},\mathbf{y})=\frac{\sigma_{xy}+C_{3}}{\sigma_{x}\sigma_{y}+C_{3}}$
(10)

where $\mu_{x}$, $\mu_{y}$, $\sigma_{x}$, $\sigma_{y}$ and $\sigma_{xy}$ are
the mean intensities of $x$ and $y$, the standard deviations of $x$ and $y$,
and correlation of $x$ and $y$, respectively. Also, $C_{i}$s are small
constants defined as $C_{i}=\left(K_{i}L\right)^{2}$, where $L$ is the dynamic
range of the pixel values and $K_{i}<<1$ ($1\leq i\leq 3$) are small
constants. Finally, SSIM index is defined as the combination of these three
quantities as follows:

$\operatorname{SSIM}(\mathbf{x},\mathbf{y})=[l(\mathbf{x},\mathbf{y})]^{\alpha}\cdot[c(\mathbf{x},\mathbf{y})]^{\beta}\cdot[s(\mathbf{x},\mathbf{y})]^{\gamma}$
(11)

where $\alpha>0$, $\beta>0$ and $\gamma>0$ adjust the relative importance of
the three components. To simplify the equation, the parameters are set as
$\alpha=\beta=\gamma=1$ and $C_{3}=C_{2}/2$ in [172] and the following
equation is obtained which is the most common form of SSIM:

$\operatorname{SSIM}(\mathbf{x},\mathbf{y})=\frac{\left(2\mu_{x}\mu_{y}+C_{1}\right)\left(2\sigma_{xy}+C_{2}\right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+C_{1}\right)\left(\sigma_{x}^{2}+\sigma_{y}^{2}+C_{2}\right)}$
(12)

This metric is used in many papers such as [91, 152, 120, 119, 144, 50, 174,
19, 100, 198, 206]. There are also some other versions of SSIM index such as
MSSIM (mean SSIM) and MS-SSIM (multiscale SSIM). MSSIM is the average SSIM of
studied images and MS-SSIM [173] considers the image details at different
resolutions. In MS-SSIM, a low-pass filter is applied to the input images and
the filtered images are iteratively downsampled by a factor of 2. The original
images are indexed as scale 1, and the highest scale is as scale $M$, obtained
after $M-1$ iterations. The contrast and structure components are calculated
for each scale $j$ as $c_{j}(x,y)$ and $s_{j}(x,y)$, respectively, while the
luminance is only computed at scale $M$ ($l_{M}(x,y)$). Finally, MS-SSIM is
obtained by the following equation:

$\operatorname{MS-
SSIM}(\mathbf{x},\mathbf{y})=\left[l_{M}(\mathbf{x},\mathbf{y})\right]^{\alpha_{M}}\cdot\prod_{j=1}^{M}\left[c_{j}(\mathbf{x},\mathbf{y})\right]^{\beta_{j}}\left[s_{j}(\mathbf{x},\mathbf{y})\right]^{\gamma_{j}}$
(13)

where $\alpha_{M}$, $\beta_{j}$ and $\gamma_{j}$, similar to equation 11
adjust the relative importance of different components [115].

  * •

PSNR is computed between two 8-bit grayscale images $x$ and $y$ [60], both of
size $M\times N$ as follows:

$\operatorname{PSNR}(x,y)=10\log_{10}\left(255^{2}/\operatorname{MSE}(x,y)\right)$
(14)

where MSE is defined as:

$\operatorname{MSE}(x,y)=\frac{1}{MN}\sum_{i=1}^{M}\sum_{j=1}^{N}\left(x_{ij}-y_{ij}\right)^{2}$
(15)

When the MSE tends to zero the PSNR will goes to infinity. This means that
whenever the high PSNR shows high quality of the generated images and low PSNR
demonstrates the high distinction between the two images. MSE is also another
metric that is used to compare a resulting image with its reference. Both of
these metrics are good at assessing the quality of the noisy images based on
the amount and intensity of the noise but do not properly reflect the
structural content of the image. Many authors have used MSE [132] and PSNR to
evaluate their proposed facial image manipulation models [91, 152, 120, 119,
144, 50, 174, 19, 100, 206].

  * •

LPIPS, introduced in [207], measures perceptual-level similarity and is
claimed to be consistent with human perception [50]. This metric is based on
the distance of the projected deep features obtained from pre-trained networks
such as VGGFace [125]. Lower values of LPIPS show that the evaluated images
are close in the feature space and similar in the perceptual level. To be more
precise, for two images $x$ and $y$ and the feature extractor network $F$,
LPIPS is computed as equation 16.

In this equation, firstly, the feature stack from $L$ layers is extracted and
unit-normalized in the dimension of the channel as
$\hat{x}^{l},\hat{y}^{l}\in\mathbb{R}^{H_{l}\times W_{l}\times C_{l}}$ for
layer $l$. Then the activations are scaled channelwise by vector
$w^{l}\in\mathbb{R}^{C_{l}}$ and the $L2$ distance is computed. Finally,
spatial averaging and channel-wise summation is applied. This metric is used
in [95, 50, 132, 91].

$d\left(y,x\right)=\sum_{l}\frac{1}{H_{l}W_{l}}\sum_{h,w}\left\|w_{l}\odot\left(\hat{y}_{hw}^{l}-\hat{x}_{hw}^{l}\right)\right\|_{2}^{2}$
(16)

IQA with networks Another approach for image quality assessment is to use a
neural network to classify edited images in two or more quality levels. Some
researchers use employ pre-trained networks for this purpose while the others
train their own domain-specific models. Both approaches are common among
quality assessment tools [156]. As well, these networks could work in full-
reference or non-reference manner. For example, DeepIQA [8] performs full
reference quality assessment while CNNIQA [71] is a network for non-reference
quality estimation.

2) Attribute edit/preservation analysis: In addition to generating high-
quality face images, facial attribute editing models should correctly fulfill
the editing task that is 1) accurately apply the requested changes to the
target attributes of the input face image, and 2) do not change the other
attributes of the face. So, the performance of a facial attribute editing
model should be assessed from these two aspects: accurate target attribute
editing and non-target attribute preserving.

Accurate Editing: To evaluate attribute editing accuracy, a multi-label
attribute classifier is employed to predict attribute values for generated
images. The editing accuracy of the model is then estimated by comparing the
attribute values of the resultant face image with the desired values. Many
works such as [58, 102, 120, 119, 174, 140, 27, 19, 100, 198, 203, 84, 199]
use this approach as a quantitative metric to assess the performance of their
attribute editing models. In the face aging models like [141, 142, 56, 191],
an age predictor model (usually the publicly available Face++ API) is used to
estimate the age of the edited image, and the aging task is assumed to have
been successfully done if the estimated age of the edited face image is the
same as the desired age group. Some special-purpose face editing models have
employed specific measures for realizing the accuracy of the modifications.
For example, [149] uses mean squared error (MSE) and Pearson’s correlation
coefficient (PCC) between action units (AUs) of input and generated images to
quantify expression manipulation accuracy. Also, Tewari et al. [152] use the
landmark space instead of the image space to measure the angular distance
between the target and generated head poses. Average $L2$ distance between 66
facial landmarks of desired and generated images is used as the alignment
error to measure the performance of the head pose editing model.

Attribute Preservation: As mentioned earlier, these group of evaluation
metrics aim to assess the ability of the facial attribute editing model in
preserving irrelevant attributes and regions of the input face image which is
also known as identity preservation property in expression and pose editing
tasks.

A common approach to this goal is to perform face verification between the
input and the modified face images. To do so, the distance between the feature
vectors of the initial and manipulated face images, extracted by pretrained
face recognition models, are measured using a Euclidean or cosine distance and
compared with a predetermined threshold to verify if the two images belong to
the same person or not as in [141, 139, 3, 115, 140, 216, 142, 141, 199].
Chang et al. [14] firstly frontalize all images and then apply the face
verification.

Unlike aforementioned works which compare the input and edited images,
Kowalski et al. [81] considers an attribute and assigns two opposite values to
it and generates a positive ($I_{+}$) and a negative ($I_{-}$) images using
the model under investigation. They then compare these two images and expect
them to differ only on the chosen attribute. In [122], attribute preservation
is assessed in three aspects: Euclidean distance between normalized 2D
landmarks of reference and output images which evaluates expression
preservation, Euclidean distance between Euler angles of reference and output
images which measures the pose preservation, and the result of face
recognition which assesses the identity preservation. Finally, Wei et al.
[174] propose a mask-aware reconstruction error to measure the ability of the
model to preserve irrelevant regions specified by the mask.

##  V Applications

Semantic facial attribute editing has a great potential to be employed in a
vast variety of real-world applications either as the whole solution or as a
part of another system. In fact, whenever a modification to a face image is
required, semantic face editing is among the best options. In this section, we
briefly review some semantic face editing usages organized by the application
areas.

###  V-A Face recognition

Face Recognition (FR) systems are ubiquitous computer vision programs in which
a face image extracted from a photo or movie frame is matched against a
database of face images of known identities. The applications of such systems
range from photo tagging to access control. The success and accessibility of
the GAN-based face editing models during the past years have stimulated many
researchers to adopt these models either to promote the performance of FR
systems or even to attack them. In the rest of this section, some applications
of face editing models in the field of face recognition are reviewed.

####  V-A1 Face data augmentation

Face data augmentation is the process of enriching a dataset of face images by
inserting new machine-created face photos from those available in the dataset.
The performance of DNN-based face processing models strongly depends on the
size and variation of their training datasets. Insufficient training data, the
lack of labeled samples, and unbalanced data distribution (dataset biases)
will cause the overfitting and low performance of these models. On the other
hand, collecting and labeling adequate samples with high quality and balanced
distributions is laborious, expensive, and error-prone. Face photo
augmentation is one of the solutions to tackle these limitations.

Face data augmentation tools can be classified into general image augmentation
techniques such as cropping, flipping, and rotation of the images, and
specific face editing methods which alter the attributes and components of the
image via sophisticated face manipulation techniques including GANs. The
augmented faces generated by controllable generative models can have specific
facial attributes and hence make it possible to reach balanced datasets with
more intra-class variations. Moreover, the ground truth labels of these new
samples are automatically generated. GAN-based face photo manipulation models
are ideal tools for face data augmentation as the edit of any facial attribute
like expression, makeup, age, eyeglasses, and hairstyle produces new precious
data for face recognition systems [134, 201, 170].

Deng et al. [32] succeeded to achieve 94-percent accuracy in face recognition
on CFP dataset due to data augmentation. In this work, the focus is on
increasing pose variation in training data by generating complete facial UV
maps via the combination of a 3D Morphable Model (3DMM) and a GAN-based deep
neural network.

In addition to face recognition, face data augmentation has found applications
in face retrieval tasks with the aim of finding all face images of a database
that are related to a specific subject. For example, it may be needed to
retrieve images of a person from an enormous database of face images captured
by CCTV cameras. As an example of such models, Deep Face Hashing with GAN
(DFH-GAN) is a hash-based approach for face image retrieval [214]. It consists
of three modules: a generator to perform data augmentation, a discriminator
with a shared CNN to learn multi-domain facial attributes, and a hash encoding
component to generate hash codes. The generator is used to generate
synthesized face images for training the main retrieval engine.

####  V-A2 Face frontalization and neutralization

Performance of the modern face recognition systems dramatically degrades with
pose and expressions of the target face image. It would be beneficial if the
input face photo is converted to a neutral frontal one before processing by
these systems. This specially gives a boost to the performance of face
recognition from uncontrolled images. The use of face attribute editing for
creating frontal neutral face images is almost straightforward. DR-GAN [155]
and TC-GAN [23] are two purpose-built models developed for this application.
DR-GAN injects changes into both generator and discriminator of the usual
GANs. The generator part has an encoder-decoder structure which empowers the
model to learn generative and discriminative representations. The
discriminator part, as in semi-supervised GANs, classifies the pose and
identity of the image in addition to real/fake discrimination. This model
calls for labeled faces to generate identity-preserving faces and labels the
face pose by one-hot codes to ensure the correctness of generated faces’
poses. TC-GAN, on the other hand, is composed of two generators $G_{c}$,
$G_{f}$, and one discriminator $D$ in a triangle network architecture. TC-GAN
converts a profile image to the corresponding frontal contour by $G_{c}$ and
then adds the face features by $G_{f}$ to generate a photo-realistic frontal
view of the input image without unintentionally changing the identity or
expression.

pSp [132] is a general image-to-image translation model which has been
successfully trained for face frontalization task with weak supervision. Here,
an encoder is trained to map the input image to the style vectors of a style-
based generator which produce the final image. The key idea of pSp is to
randomly flip training images that forces the model to learn a fixed frontal
image close to both states of the input image. In another attempt, Luan et al.
[110] focus on facial pattern preserving in their face frontalization and
recognition system and try to reach this goal by designing a discriminator
which leverages self-attention blocks and a series of sub-discriminators. In
another attempt in this field, a self-attention-based generator and a face-
attention-based discriminator are proposed for long-pose face
frontalization[197]. The generator combines local features with long-range
dependencies, and therefore, produces identity-preserved faces while the
discriminator highlights local features of face regions and consequently
strengthens the realism of generated frontal faces.

####  V-A3 De-identification

The goal of face de-identification is to remove the identity of a face in a
photo or video in order to preserve the privacy of individuals while the other
attributes (e.g. sex, age, pose, expression, and illumination) remain
unchanged[12]. For example, though collecting huge datasets of images or
videos in the wild is a necessary step in most of the computer vision tasks,
protecting the privacy of the people captured in these images/videos is
indispensable too. The need for reliable de-identification tools becomes more
apparent when large image collections are going to be published on web either
as a training set like ImageNet [188] or through an application like Google
Street View.

Blurring the face areas of the people in an image is a simple yet effective
way for hiding the identities of the face images but these obfuscated faces
render the images unpleasant for human users. A more intelligent way for
performing de-identification is to replace the original face with a randomly
synthesized face fitting the target person’s body. In [96], the face photos of
a number of donors are used for replacing the target faces and a facial
attribute transfer model is used to map non-identity facial attributes of the
original face to the donor’s face photo. In a different approach, PP-GAN [180]
trains a U-net based generator for mapping an input face image to a de-
identified face with similar structure. For training this generator, in
addition to the original discriminator, a verificator and a regulator
component are used. The former provides a contrastive loss based on the
identities of the two faces and the latter gives the structural similarity
loss to measure the similarity of two faces.

###  V-B Video generation

Recently deepfake videos, as a direct outcome of the facial attribute editing
models, have received great attention in social neural networks. The possible
applications of facial attribute editing in the movie and animation industries
are enormous and here we will look at some of them.

####  V-B1 Movie, animation and deepfake production

As mentioned before, several generative models have been proposed for
transferring style from a so-called driver to a target image to create moving
characters. The movement may happen in the lips, face, head or the full body
and has caused great attention towards these models due to their applicability
in animation, realistic animation and movie (deepfake) production. In the
following, we will look at just a few examples of the large body of literature
devoted to these applications.

Deepfake refers to the use of deep learning techniques for creating fake
images or vides, hence the name. In fact, deepfake is a kind of image/video
manipulation which has experienced tremendous progress during the past years
thanks to the rapid development of generative models, especially GANs.
Deepfakes are generated in different ways including entire face swap,
expression manipulation, face and body reenactment, and background editing.
Regarding the face swap models, RSGAN [115] makes use of two distinct networks
to separately learn latent space representations of face and hair. It then
uses a composer component to reconstruct swapped faces by combining the hair
of one person with the face of the other. In addition to the adversarial loss,
the composer is trained with another discriminator that checks if the local
patches of the input images are realistic or not. FaceShifter [92] is another
prominent face swap model based on a two-stage framework which also deals with
facial occlusions. The first stage generates swapped faces using an identity
encoder, a multi-level attributes encoder, and an Adaptive Attentional
Denormalization (AAD) generator. The information of the two first components
are integrated in the ADD-generator to create swapped faces. The main focus of
the second stage of FaceShifter is on addressing the occlusion problem where a
Heuristic Error Acknowledging Refinement Network (HEAR-Net) is designed to
solve this issue by the use of a face segmentation network in a fully self-
supervised way.

Expression editing is the other method of creating fake images and videos in
which an input image is modified to have a particular expression, e.g.
furious, happy, or sad. For example, Wu et al. [177] progressively manipulate
the expression of face by cascading EF-GAN blocks each composed of two
components: an expression transformer and a refiner. The expression
transformer has four branches, one branch for global and three others for eye,
nose, and mouth, expression manipulation. The refiner combines outputs of the
expression transformer and refines them to the final editing. In some models,
as in Labeled Free Expression Editing (LEED) [176], the input image is first
mapped to a corresponding neutral face and then the target expression is added
to it.

Motion transfer is almost the most popular technique among deepfake generators
which has also caught the attention of movie and animation producers. As
stated above, here, the motion patterns of a driver are transferred to the
target person while the face identity attributes and body shapes remain
intact. Face reenactment is a common form of motion transfer which may be used
to produce a deepfake speech from a politician, revolutionize the movie
dubbing industry or create a talking head from a picture of a dead person.
Tripathy et al. [156] developed a face reenactment model which is trained on
frames of videos and learns to transfer facial non-identity attributes from
one face image to another image of the same identity at the train time but to
a different identity in the test phase. The model, named ICface, is based on
two generators: one for generating a neutral image from the target person and
one for manipulating it as per the extracted non-identity attributes of the
input face. As another example of these models, DAE-GAN [200] learns separate
pose and identity representations for generating talking faces. A conditional
GAN is then employed to perform the style transformation.

####  V-B2 Text or speech to video

Automatic video generation has become a promising line of research. While the
current generative models are still far from generating the whole body of
characters with high resolution and all details, talking face video generation
as a special case of video generation seems to be reachable by the face
attribute manipulating models. Recently, generating talking faces based on the
input text or audio have received attention due to its applicability in lip
synchronization in dubbing and deepfake generation[109]. Chen et al. [16]
propose a temporal GAN network for generating audio-driven talking face
videos. Their model firstly generates landmarks corresponding to the input
audio and then synthesizes the desired output based on the generated
landmarks. Yi et al. [192] have also introduced a similar model and have
attempted to minimize the artifacts produced due to head rotation. To tackle
this problem, they utilize a 3D face reconstruction network and a memory-
augmented GAN. In addition to the generator and discriminator, the memory-
augmented GAN has a memory network for remembering representative identity
features during training. At the test time, the memory network retrieves best-
match features and sends them to the generator. This model is capable to
generate videos with personalized head poses. Text2Video [208] is another
network that generates a video from a text source. In Text2Video, the input
text is first converted to audio and in parallel, a series of landmarks are
generated from input text by a phoneme-pose dictionary. Finally, by the help
of vid2vid GAN, the synthesized audio and the landmarks series are transferred
to the target talking face video.

###  V-C Face construction and reconstruction

As mentioned earlier, in many applications, it is required to create a human
either randomly or based on a description as in virtual reality generation,
movie and animation production, de-identification and facial composite
creation by forensic technicians. In most of these applications, the target
face is fully or partially determined by a set of facial attribute values or a
textual description. A straightforward strategy in these cases is to create an
initial portrait and then adjust or manipulate it based on the modifications
suggested by the person having the final face in his mind, e.g. the producer
of movie or an eye-witness of a scene. In the following, some applications of
this scope are briefly reviewed.

####  V-C1 Cartoon generation

The transformation of human photos to cartoon characters, comic book
characters, caricature portraits or emoji faces is an interesting application
of AI powered tools for social media users. The result is ready with just few
clicks without any laborious or expensive manual work. The task may be
formulated as an image-to-image translation with probable extra editing
required by the users [127]. As expected, GAN based models are ideal tools for
the creation and manipulation of such images. One class of existing works like
CartoonGAN [20], U-GAT-IT [79], AutoToon [45], and ReStyle [5] just translate
a real face/scene photo to a cartoon-domain one. The other methods, like the
one proposed by Wang et al. [163], allow the edit of the face attributes in
both real and cartoon domains. In this model, the StyleGAN2 model is fine
tuned to generate cartoon faces from the latent style vectors. Regarding the
attribute manipulation, two networks are employed for mapping the latent codes
and the corresponding 3DMM parameters to each other and the attribute editing
is performed on the 3DMM face model.

####  V-C2 3D face reconstruction

3D face reconstruction is the task of creating a 3D model, e.g. 3D Morphable
Model (3DMM), or a 3D mesh of a person from a photograph of his. These 3D
models provide a basis for performing different edits such as changing the
pose of the head or applying various lighting conditions, hence considered as
a prerequisite of many face manipulation related applications like virtual
reality. Luo et al. [111] have introduced a GAN-based framework for learning a
model that can generate a 3D relightable and animation-friendly avatar face
from a single unconstrained photograph. Their framework is trained in two
inference and refinement stages. At the first stage, they combine a non-linear
3DMM with StyleGAN2 to generate a normalized 3D face. At the second stage,
they attempt to refine the model by reconstructing the input face image from
the 3D face model generated in the first stage and the face-mask corresponding
to the input face which is extracted by PSPNet. The final output of the model
is a textured 3D face with neutral expression and normalized lighting
conditions that can be used for further modifications. Lee et al. [89] have
also designed an encoder-decoder structured model for the same purpose. The
uncertainty-aware encoder extracts features and their distribution from the
input face photos and the decoder is a combination of Graph CNNs and GANs to
reconstruct high-quality 3D faces from single in-the-wild face images. Yin et
al. [196] reconstruct 3D faces by designing a generator for UV maps which are
2D representations of the 3D models that can be learned by GANs. This model
utilizes a UV sampler to synthesize the incomplete UV maps from the input
face. Then, the generator network generates a complete $UV_{perd}$ map from
the incomplete one.

###  V-D Face photo post processing

####  V-D1 Makeup and de-makeup

Prominent makeup applications (e.g., Taaz and PortraintPro) can transfer
selected makeup styles to a face photo, but the set of transferable makeup
styles are limited and preconfigured. GAN-based face manipulation models have
paved the way for creating more effective makeup transfer apps in which any
makeup style from an input face can be transmitted to a target photo.
BeautyGAN’s [94] is one of these tools that has cycle-GAN architecture and
with its two cooperating GANs applies the makeup of a face image to another
face photo. Nguyen et al. [117] extract UV maps from two inputs, then by
transferring the color and pattern from makeup style to target image, generate
the desired output. SCGAN [31] splits makeup style into three parts (i.e. lip,
eyes, and skin), making it possible to have makeup style transfer in different
regions of the face.

Some makeup effects make severe changes in facial appearance that causes a
mismatch between the face with and without makeup. Forasmuch as this mismatch
affects the performance of automatic face recognition systems used in security
applications, de-makeup tools are also required for removing makeups from the
input images. BTD-net [10] is a bidirectional makeup/de-makeup network that
can learn on unpaired data. This model uses one generator and one
discriminator in each direction and is specially designed for removing makeup
effects from the input face photos.

####  V-D2 Accessory removing and adding

As stated early in this paper (Section II), the existence of fashionable or
necessary face accessories, (e.g., hat, glasses, sunglasses, jewelry, and
mask) in a face photo is regarded as a facial attribute. Most of the general
facial attribute editing methods studied in this paper support the addition or
removal of some sorts of accessories, e.g. glasses – an interesting
application for accessory suppliers and of course for the general public. The
removal of face accessories is also important in face recognition systems as
these accessories affect the representation of the face by the corresponding
models and degrades the performance of the recognition algorithms. One
relatively successful model for this aim is ByeGlassesGAN [90] whose generator
consists of an encoder-decoder and a segmentation decoder. The encoder
extracts non-accessory information of face to be used by the decoder to
reconstruct the input face without accessories and the segmentation decoder
tries to locate the region of eyeglasses on the input face and predict the
corresponding mask to guide the reconstruction process. It is shown that a
significant increase in the face recognition accuracy is achieved by the use
of ByeGlassesGAN.

####  V-D3 Aging and rejuvenation

Age is a global continuous feature of a face and as any other attribute may be
the subject of facial attribute editing. However, manipulating a face photo so
as to look younger or older is a complicated task that requires sophisticated
changes of the image. Recently, GAN-based aging applications have become quite
popular among the users of social networks. For example, Or et al. [123] have
classified the age range of 0 to 70 into 6 intervals and have trained a model
to perform age progression and regression on the input face image. Due to the
need for generating images in different age groups, their model has a
conditional generator which is a multi-domain image-to-image translation
network. Yao et al. [191] focus on the resolution of the generated age-edited
faces. They use an encoder-decoder structure along with an age modulation
layer. The encoder and decoder attempt to learn an age-invariant latent space,
while the age modulation layer codes the target age into a 128-dimensional
vector which is applied to the decoder. A3GAN [106] consists of an attribute-
aware attentive generator, a wavelet-based multi-pathway discriminator, and a
facial attribute embedding network. The generator learns to generate elderly
face images by the use of a discriminator that works on multi-scale wavelet
coefficients and learns to identify generated images from real ones. The role
of the facial attribute embedding network is to decrease the matching
ambiguity that exists in unpaired data. Both generator and discriminator use
the output of the facial attribute embedding network. A3GAN is a
unidirectional model and does not work for rejuvenation.

##  VI Challenges

Despite all of the efforts put into the development of semantic facial
attribute editing models, there is still a tremendous amount of work to do to
achieve ideal models for this purpose. In this section, the most important
challenges of the face attribute editing tasks along with the limitations of
the current state-of-the-art models in facing these challenges are discussed.

###  VI-A Attribute entanglement

Attribute entanglement is one of the most well-studied challenges of face
image generating and facial attribute manipulation methods. In this
phenomenon, altering the value of an attribute through a model also changes
other attributes. For example, by requesting a model to increase the age of a
person, glasses are also added to the image. This attribute entanglement
either comes from the biological relation between the attributes (e.g. race
and skin color) or is a result of the biases present in the datasets used for
training the models (e.g. gender and smile) [54].

Regardless of the origin of these connections between attributes, it is
desired to have models capable of independently manipulating each facial
attribute. Learning disentangled latent representation for face images has
been a hot topic during the past years, and many of the works studied in the
previous sections claim to have this property. However, attribute entanglement
remains a demanding challenge in this realm.

###  VI-B Uncommon face conditions

A close look at the facial attribute editing models reviewed in this paper
reveals that most of these models can be applied only to face images similar
to their training images. Most of the images of these datasets are captured in
common conditions including a frontal pose without extreme pose or
expressions, and not severely covered by body parts or external objects. As a
result, when you ask the current stat-of-the-art models to add smile to a side
view picture (profile shot) of a person the outcome is not satisfactory. The
same happens for any unusual face photo with few or non similar images in the
training dataset. Except for some special models like FaceShifter [92], the
face attribute manipulation models can not separate the occluding objects from
the face behind them and produce degenerated images in the presence of
occluding objects.

###  VI-C Low data resources

As mentioned earlier, almost all of the available facial attribute editing
models employ deep data-hungry neural networks as their encoders and decoders.
The whole knowledge of these models about the human face comes from their
training data and their generated images follow the statistical distribution
of these data. A complete list of the datasets used for training these models
was provided in Table II. These datasets suffer from different shortcomings.
First of all, these datasets are not diverse enough, for example, many human
races are not covered or some combinations of attributes are not present in
these datasets. The other drawback of the current datasets is their innate
biases. For example, most of the sad face photos belong to the kids. Moreover,
the attribute tags are imbalanced for example there are a large number of
smiling and neutral faces while there are very small angry faces. As much of
these datasets are collected from the web or under the common situations, the
unusual face statuses discussed in the previous subsection like extreme poses
and expressions and occluding objects are not covered by them. Finally, the
number of images and the quality of these images are two important factors of
a proper dataset for training facial attribute editing models.

###  VI-D Evaluation metrics

A number of quantitative and qualitative measures used for evaluating the
performance of facial attribute editing models were reviewed in Section IV.
The qualitative metrics are based on human judgements and the quantitative
measures either estimate the quality of the generated image or the rate of the
presence/absence of the target attribute. In fact, all of these measures are
some proxies for the required evaluation metric, i.e., how good is the
requested edit performed? The investigation of the results by humans is still
the most common metric used in this field and no quantitative special-purpose
metric has been proposed for evaluating the outcome of a face attribute
editing model.

###  VI-E Quality of the edited/generated images

Despite the great improvements in face generation and manipulation methods,
the resolution and quality of the generated images are not still enough for
many real-world applications[193]. Most of the studied models perform edit on
low-resolution images. For example, the size of the input images of ICface
[156] and AttGAN [58] are $128\times 128$ and $384\times 384$, respectively.
Most of the other models with higher output resolutions, e.g., [181], are
based on the pre-trained face generators like StyleGAN. Moreover, the images
generated by these methods are still prone to various artifacts and
deficiencies. Improving the resolution and quality of the edited images
requires larger datasets and higher computational power for training the
models.

##  VII Conclusion

Facial attribute editing has received great attention during the past years
due to its wide range of applications in different areas. Following the recent
successes in generative adversarial networks and deep neural networks, a large
number of models have been proposed for semantic facial attribute editing. In
this paper, we provided a comprehensive survey of these work and tried to
investigate them from different perspectives. In these models, the target
attributes are usually given by an attribute vector or a driving image. Each
of the proposed models generally follow one of the three main architectures of
this field, namely encoder-decoder, image-to-image translation, and photo-
guided methods. The models are trained on paired or unpaired datasets using a
diverse set of loss functions including reconstruction, attribute
classification, and adversarial losses. Despite invaluable achievements, the
current state-of-the-art models yet are unable to overcome all challenges of
the semantic editing task. Low resolution and image quality, attribute
entanglement, and working only on common face images are the most important of
the drawbacks of the current models. Preparing large unbiased labeled datasets
of facial images, defining special-purpose metrics for evaluation, and
learning disentangled representations seems to be important requirements of
the current models.

## References

  * Abdal et al. [2019] Abdal, R., Qin, Y., Wonka, P., 2019.  Image2stylegan: How to embed images into the stylegan latent space?, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4432–4441.
  * Abdal et al. [2020] Abdal, R., Qin, Y., Wonka, P., 2020.  Image2stylegan++: How to edit the embedded images?, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8296–8305.
  * Abdal et al. [2021] Abdal, R., Zhu, P., Mitra, N.J., Wonka, P., 2021.  Styleflow: Attribute-conditioned exploration of stylegan-generated images using conditional continuous normalizing flows.  ACM Transactions on Graphics (TOG) 40, 1–21.
  * Akhtar et al. [2019] Akhtar, Z., Dasgupta, D., Banerjee, B., 2019.  Face authenticity: An overview of face manipulation generation, detection and recognition, in: Nutan College of Engineering & Research, International Conference on Communication and Information Processing (ICCIP).
  * Alaluf et al. [2021] Alaluf, Y., Patashnik, O., Cohen-Or, D., 2021.  Restyle: A residual-based stylegan encoder via iterative refinement, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6711–6720.
  * Ali and Hughes [2020] Ali, K., Hughes, C.E., 2020\.  An efficient integration of disentangled attended expression and identity featuresfor facial expression transfer andsynthesis.  arXiv preprint arXiv:2005.00499 .
  * Awiszus et al. [2019] Awiszus, M., Ackermann, H., Rosenhahn, B., 2019.  Learning disentangled representations via independent subspaces, in: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 0–0.
  * Bosse et al. [2017] Bosse, S., Maniry, D., Müller, K.R., Wiegand, T., Samek, W., 2017.  Deep neural networks for no-reference and full-reference image quality assessment.  IEEE Transactions on image processing 27, 206–219.
  * Buolamwini and Gebru [2018] Buolamwini, J., Gebru, T., 2018\.  Gender shades: Intersectional accuracy disparities in commercial gender classification, in: Conference on fairness, accountability and transparency, pp. 77–91.
  * Cao et al. [2019] Cao, C., Lu, F., Li, C., Lin, S., Shen, X., 2019.  Makeup removal via bidirectional tunable de-makeup network.  IEEE Transactions on Multimedia 21, 2750–2761.
  * Cao et al. [2013] Cao, C., Weng, Y., Zhou, S., Tong, Y., Zhou, K., 2013\.  Facewarehouse: A 3d facial expression database for visual computing.  IEEE Transactions on Visualization and Computer Graphics 20, 413–425.
  * Cao et al. [2021] Cao, J., Liu, B., Wen, Y., Xie, R., Song, L., 2021\.  Personalized and invertible face de-identification by disentangled identity information manipulation, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3334–3342.
  * Chandaliya et al. [2019] Chandaliya, P.K., Kumar, V., Harjani, M., Nain, N., 2019\.  Scdae: Ethnicity and gender alteration on clf and utkface dataset, in: International Conference on Computer Vision and Image Processing, Springer. pp. 294–306.
  * Chang et al. [2018] Chang, H., Lu, J., Yu, F., Finkelstein, A., 2018.  Pairedcyclegan: Asymmetric style transfer for applying and removing makeup, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 40–48.
  * Chen et al. [2014] Chen, B.C., Chen, C.S., Hsu, W.H., 2014.  Cross-age reference coding for age-invariant face recognition and retrieval, in: European conference on computer vision, Springer. pp. 768–783.
  * Chen et al. [2019a] Chen, L., Zheng, H., Maddox, R.K., Duan, Z., Xu, C., 2019a.  Sound to visual: Hierarchical cross-modal talking face video generation, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops.
  * Chen et al. [2020a] Chen, L., Zhou, G., Liu, Y., Jin, J., 2020a.  Overview of face attribute generation, in: 2020 Chinese Control And Decision Conference (CCDC), IEEE. pp. 610–615.
  * Chen et al. [2020b] Chen, P., Xiao, Q., Xu, J., Dong, X., Sun, L., Li, W., Ning, X., Wang, G., Chen, Z., 2020b.  Harnessing semantic segmentation masks for accurate facial attribute editing.  Concurrency and Computation: Practice and Experience , e5798.
  * Chen et al. [2020c] Chen, X., Ni, B., Liu, N., Liu, Z., Jiang, Y., Truong, L., Tian, Q., 2020c.  Coogan: A memory-efficient framework for high-resolution facial attribute editing, in: European Conference on Computer Vision, Springer. pp. 670–686.
  * Chen et al. [2018a] Chen, Y., Lai, Y.K., Liu, Y.J., 2018a.  Cartoongan: Generative adversarial networks for photo cartoonization, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 9465–9474.
  * Chen et al. [2018b] Chen, Y.C., Lin, H., Shu, M., Li, R., Tao, X., Shen, X., Ye, Y., Jia, J., 2018b.  Facelet-bank for fast portrait manipulation, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3541–3549.
  * Chen et al. [2019b] Chen, Y.C., Shen, X., Lin, Z., Lu, X., Pao, I., Jia, J., et al., 2019b.  Semantic component decomposition for face attribute manipulation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9859–9867.
  * Cheng et al. [2019] Cheng, J., Chen, Y.P.P., Li, M., Jiang, Y.G., 2019\.  Tc-gan: Triangle cycle-consistent gans for face frontalization with facial features preserved, in: Proceedings of the 27th ACM International Conference on Multimedia, pp. 220–228.
  * Cho et al. [2019] Cho, W., Choi, S., Park, J., Park, D.K., Qin, T., Choo, J., 2019.  What and where to translate: Local mask-based image-to-image translation.  arXiv preprint arXiv:1906.03598 .
  * Choi et al. [2018] Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., Choo, J., 2018.  Stargan: Unified generative adversarial networks for multi-domain image-to-image translation, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8789–8797.
  * Choi et al. [2020] Choi, Y., Uh, Y., Yoo, J., Ha, J.W., 2020.  Stargan v2: Diverse image synthesis for multiple domains, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8188–8197.
  * Chu et al. [2020] Chu, W., Tai, Y., Wang, C., Li, J., Huang, F., Ji, R., 2020.  Sscgan: Facial attribute editing via style skip connections, in: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XV 16, Springer. pp. 414–429.
  * Collins et al. [2020] Collins, E., Bala, R., Price, B., Susstrunk, S., 2020\.  Editing in style: Uncovering the local semantics of gans, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5771–5780.
  * Damer et al. [2018] Damer, N., Saladie, A.M., Braun, A., Kuijper, A., 2018\.  Morgan: Recognition vulnerability and attack detectability of face morphing attacks created by generative adversarial network, in: 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS), IEEE. pp. 1–10.
  * Deb et al. [2018] Deb, D., Nain, N., Jain, A.K., 2018.  Longitudinal study of child face recognition, in: 2018 International Conference on Biometrics (ICB), IEEE. pp. 225–232.
  * Deng et al. [2021] Deng, H., Han, C., Cai, H., Han, G., He, S., 2021\.  Spatially-invariant style-codes controlled makeup transfer, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6549–6557.
  * Deng et al. [2018] Deng, J., Cheng, S., Xue, N., Zhou, Y., Zafeiriou, S., 2018\.  Uv-gan: Adversarial facial uv map completion for pose-invariant face recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7093–7102.
  * Deng et al. [2020] Deng, Q., Cao, J., Liu, Y., Chai, Z., Li, Q., Sun, Z., 2020.  Reference guided face component editing.  arXiv preprint arXiv:2006.02051 .
  * Ding et al. [2020] Ding, C., Kang, W., Zhu, J., Du, S., 2020.  Injectiongan: Unified generative adversarial networks for arbitrary image attribute editing.  IEEE Access 8, 117726–117735.
  * Dogan and Keles [2020] Dogan, Y., Keles, H.Y., 2020\.  Semi-supervised image attribute editing using generative adversarial networks.  Neurocomputing 401, 338–352.
  * Du et al. [2014] Du, S., Tao, Y., Martinez, A.M., 2014.  Compound facial expressions of emotion.  Proceedings of the National Academy of Sciences 111, E1454–E1462.
  * Esser et al. [2020] Esser, P., Rombach, R., Ommer, B., 2020.  A disentangling invertible interpretation network for explaining latent representations, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9223–9232.
  * Fabian Benitez-Quiroz et al. [2016] Fabian Benitez-Quiroz, C., Srinivasan, R., Martinez, A.M., 2016.  Emotionet: An accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5562–5570.
  * Fang et al. [2020] Fang, H., Deng, W., Zhong, Y., Hu, J., 2020.  Triple-gan: Progressive face aging with triple translation loss, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 804–805.
  * Friesen and Ekman [1978] Friesen, E., Ekman, P., 1978\.  Facial action coding system: a technique for the measurement of facial movement.  Palo Alto 3, 5.
  * Fu et al. [2021] Fu, C., Hu, Y., Wu, X., Wang, G., Zhang, Q., He, R., 2021.  High-fidelity face manipulation with extreme poses and expressions.  IEEE Transactions on Information Forensics and Security 16, 2218–2231.
  * Fu et al. [2014] Fu, Y., Hospedales, T.M., Xiang, T., Gong, S., Yao, Y., 2014.  Interestingness prediction by robust learning to rank, in: European conference on computer vision, Springer. pp. 488–503.
  * Gao et al. [2007] Gao, W., Cao, B., Shan, S., Chen, X., Zhou, D., Zhang, X., Zhao, D., 2007\.  The cas-peal large-scale chinese face database and baseline evaluations.  IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 38, 149–161.
  * Ghosh et al. [2020] Ghosh, P., Gupta, P.S., Uziel, R., Ranjan, A., Black, M.J., Bolkart, T., 2020\.  Gif: Generative interpretable faces, in: 2020 International Conference on 3D Vision (3DV), IEEE. pp. 868–878.
  * Gong et al. [2020] Gong, J., Hold-Geoffroy, Y., Lu, J., 2020.  Autotoon: Automatic geometric warping for face cartoon generation, in: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 360–369.
  * Goodfellow et al. [2014] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y., 2014\.  Generative adversarial nets.  Advances in neural information processing systems 27\.
  * Gross et al. [2010] Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S., 2010.  Multi-pie.  Image and Vision Computing 28, 807–813.
  * Gu et al. [2019a] Gu, Q., Wang, G., Chiu, M.T., Tai, Y.W., Tang, C.K., 2019a.  Ladn: Local adversarial disentangling network for facial makeup and de-makeup, in: Proceedings of the IEEE International Conference on Computer Vision, pp. 10481–10490.
  * Gu et al. [2019b] Gu, S., Bao, J., Yang, H., Chen, D., Wen, F., Yuan, L., 2019b.  Mask-guided portrait editing with conditional gans, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3436–3445.
  * Guan et al. [2020] Guan, S., Tai, Y., Ni, B., Zhu, F., Huang, F., Yang, X., 2020.  Collaborative learning for faster stylegan embedding.  arXiv preprint arXiv:2007.01758 .
  * Guo et al. [2019] Guo, J., Qian, Z., Zhou, Z., Liu, Y., 2019.  Mulgan: Facial attribute editing by exemplar.  arXiv preprint arXiv:1912.12396 .
  * Guo et al. [2020] Guo, Y., Shang, Y., Zhang, X., 2020.  Hierarchical neural network for facial attribute transfer.  MS&E 768, 072088\.
  * Ha et al. [2020] Ha, S., Kersner, M., Kim, B., Seo, S., Kim, D., 2020\.  Marionette: Few-shot face reenactment preserving identity of unseen targets, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 10893–10900.
  * Han et al. [2021] Han, Y., Yang, J., Fu, Y., 2021.  Disentangled face attribute editing via instance-aware latent space search, in: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21), pp. 715–721.
  * He et al. [2019a] He, T., Xia, Y., Lin, J., Tan, X., He, D., Qin, T., Chen, Z., 2019a.  Deliberation learning for image-to-image translation., in: IJCAI, pp. 2484–2490.
  * He et al. [2019b] He, Z., Kan, M., Shan, S., Chen, X., 2019b.  S2gan: Share aging factors across ages and share aging trends among individuals, in: Proceedings of the IEEE International Conference on Computer Vision, pp. 9440–9449.
  * He et al. [2020] He, Z., Kan, M., Zhang, J., Shan, S., 2020.  Pa-gan: Progressive attention generative adversarial network for facial attribute editing.  arXiv preprint arXiv:2007.05892 .
  * He et al. [2019c] He, Z., Zuo, W., Kan, M., Shan, S., Chen, X., 2019c.  Attgan: Facial attribute editing by only changing what you want.  IEEE Transactions on Image Processing 28, 5464–5478.
  * Heusel et al. [2017] Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S., 2017.  Gans trained by a two time-scale update rule converge to a local nash equilibrium.  Advances in neural information processing systems 30\.
  * Hore and Ziou [2010] Hore, A., Ziou, D., 2010.  Image quality metrics: Psnr vs. ssim, in: 2010 20th international conference on pattern recognition, IEEE. pp. 2366–2369.
  * Hsu et al. [2018] Hsu, S.Y., Yang, C.Y., Huang, C.C., Hsu, J.Y.j., 2018\.  Semistargan: Semi-supervised generative adversarial networks for multi-domain image-to-image translation, in: Asian Conference on Computer Vision, Springer. pp. 338–353.
  * Hu et al. [2020] Hu, S., Shum, H.P., Liang, X., Li, F.W., Aslam, N., 2020\.  Facial reshaping operator for controllable face beautification.  Expert Systems with Applications , 114067\.
  * Huang et al. [2008] Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E., 2008\.  Labeled faces in the wild: A database forstudying face recognition in unconstrained environments, in: Workshop on faces in’Real-Life’Images: detection, alignment, and recognition.
  * Huang et al. [2018a] Huang, H., He, R., Sun, Z., Tan, T., et al., 2018a.  Introvae: Introspective variational autoencoders for photographic image synthesis, in: Advances in neural information processing systems, pp. 52–63.
  * Huang et al. [2020] Huang, P.H., Yang, F.E., Wang, Y.C.F., 2020.  Learning identity-invariant motion representations for cross-id face reenactment, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7084–7092.
  * Huang et al. [2018b] Huang, X., Liu, M.Y., Belongie, S., Kautz, J., 2018b.  Multimodal unsupervised image-to-image translation, in: Proceedings of the European Conference on Computer Vision (ECCV), pp. 172–189.
  * Isola et al. [2017] Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A., 2017\.  Image-to-image translation with conditional adversarial networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1125–1134.
  * Jaderberg et al. [2015] Jaderberg, M., Simonyan, K., Zisserman, A., et al., 2015.  Spatial transformer networks.  Advances in neural information processing systems 28, 2017–2025.
  * Jordan et al. [2002] Jordan, A., et al., 2002.  On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes.  Advances in neural information processing systems 14, 841.
  * Juefei-Xu et al. [2021] Juefei-Xu, F., Wang, R., Huang, Y., Guo, Q., Ma, L., Liu, Y., 2021.  Countering malicious deepfakes: Survey, battleground, and horizon.  arXiv preprint arXiv:2103.00218 .
  * Kang et al. [2014] Kang, L., Ye, P., Li, Y., Doermann, D., 2014.  Convolutional neural networks for no-reference image quality assessment, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1733–1740.
  * Kang and Hahn [2021] Kang, S., Hahn, T., 2021.  Eyeglass remover network based on a synthetic image dataset.  KSII Transactions on Internet & Information Systems 15.
  * Kapania et al. [2019] Kapania, S., Goyal, S., Lamba, S., Gupta, N., Gupta, S., 2019.  Multiple domain image to image translation for facial attribute transfer.  International Journal of Information Systems & Management Science 2.
  * Kärkkäinen and Joo [2019] Kärkkäinen, K., Joo, J., 2019\.  Fairface: Face attribute dataset for balanced race, gender, and age.  arXiv preprint arXiv:1908.04913 .
  * Karras et al. [2018] Karras, T., Aila, T., Laine, S., Lehtinen, J., 2018\.  Progressive growing of gans for improved quality, stability, and variation, in: International Conference on Learning Representations.
  * Karras et al. [2019] Karras, T., Laine, S., Aila, T., 2019.  A style-based generator architecture for generative adversarial networks, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4401–4410.
  * Karras et al. [2020] Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T., 2020\.  Analyzing and improving the image quality of stylegan, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8110–8119.
  * Kim et al. [2021] Kim, D., Khan, M.A., Choo, J., 2021.  Not just compete, but collaborate: Local image-to-image translation via cooperative mask prediction, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6509–6518.
  * Kim et al. [2019] Kim, J., Kim, M., Kang, H., Lee, K.H., 2019.  U-gat-it: Unsupervised generative attentional networks with adaptive layer-instance normalization for image-to-image translation, in: International Conference on Learning Representations.
  * Klare et al. [2015] Klare, B.F., Klein, B., Taborsky, E., Blanton, A., Cheney, J., Allen, K., Grother, P., Mah, A., Jain, A.K., 2015.  Pushing the frontiers of unconstrained face detection and recognition: Iarpa janus benchmark a, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1931–1939.
  * Kowalski et al. [2020] Kowalski, M., Garbin, S.J., Estellers, V., Baltrušaitis, T., Johnson, M., Shotton, J., 2020\.  Config: Controllable neural face image generation, in: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XI 16, Springer. pp. 299–315.
  * Kumar et al. [2008] Kumar, N., Belhumeur, P., Nayar, S., 2008.  Facetracer: A search engine for large collections of images with faces, in: European conference on computer vision, Springer. pp. 340–353.
  * Kumar et al. [2009] Kumar, N., Berg, A.C., Belhumeur, P.N., Nayar, S.K., 2009\.  Attribute and simile classifiers for face verification, in: 2009 IEEE 12th international conference on computer vision, IEEE. pp. 365–372.
  * Kwak et al. [2020] Kwak, J.g., Han, D.K., Ko, H., 2020.  Cafe-gan: Arbitrary face attribute editing with complementary attention feature, in: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIV 16, Springer. pp. 524–540.
  * Lample et al. [2017] Lample, G., Zeghidour, N., Usunier, N., Bordes, A., Denoyer, L., Ranzato, M., 2017\.  Fader networks: Manipulating images by sliding attributes, in: Advances in neural information processing systems, pp. 5967–5976.
  * Langner et al. [2010] Langner, O., Dotsch, R., Bijlstra, G., Wigboldus, D.H., Hawk, S.T., Van Knippenberg, A., 2010\.  Presentation and validation of the radboud faces database.  Cognition and emotion 24, 1377–1388.
  * Le et al. [2012] Le, V., Brandt, J., Lin, Z., Bourdev, L., Huang, T.S., 2012\.  Interactive facial feature localization, in: European conference on computer vision, Springer. pp. 679–692.
  * Lee et al. [2020] Lee, C.H., Liu, Z., Wu, L., Luo, P., 2020.  Maskgan: Towards diverse and interactive facial image manipulation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5549–5558.
  * Lee and Lee [2020] Lee, G.H., Lee, S.W., 2020.  Uncertainty-aware mesh decoder for high fidelity 3d face reconstruction, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6100–6109.
  * Lee and Lai [2020] Lee, Y.H., Lai, S.H., 2020.  Byeglassesgan: Identity preserving eyeglasses removal for face images, in: European Conference on Computer Vision, Springer. pp. 243–258.
  * Li et al. [2020] Li, D., Zhang, M., Zhang, L., Chen, W., Feng, G., 2020\.  A novel attribute-based generation architecture for facial image editing.  Multimedia Tools and Applications , 1–22.
  * Li et al. [2019a] Li, L., Bao, J., Yang, H., Chen, D., Wen, F., 2019a.  Faceshifter: Towards high fidelity and occlusion aware face swapping.  arXiv preprint arXiv:1912.13457 .
  * Li and Lin [2019] Li, T., Lin, L., 2019.  Anonymousnet: Natural face de-identification with measurable privacy, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 0–0.
  * Li et al. [2018] Li, T., Qian, R., Dong, C., Liu, S., Yan, Q., Zhu, W., Lin, L., 2018.  Beautygan: Instance-level facial makeup transfer with deep generative adversarial network, in: Proceedings of the 26th ACM international conference on Multimedia, pp. 645–653.
  * Li et al. [2019b] Li, X., Hu, J., Zhang, S., Hong, X., Ye, Q., Wu, C., Ji, R., 2019b.  Attribute guided unpaired image-to-image translation with semi-supervised learning.  arXiv preprint arXiv:1904.12428 .
  * Li and Lyu [2019] Li, Y., Lyu, S., 2019.  De-identification without losing faces, in: Proceedings of the ACM Workshop on Information Hiding and Multimedia Security, pp. 83–88.
  * Lin et al. [2019] Lin, J., Chen, Z., Xia, Y., Liu, S., Qin, T., Luo, J., 2019.  Exploring explicit domain supervision for latent space disentanglement in unpaired image-to-image translation.  IEEE transactions on pattern analysis and machine intelligence .
  * Ling et al. [2020] Ling, J., Xue, H., Song, L., Yang, S., Xie, R., Gu, X., 2020.  Toward fine-grained facial expression manipulation, in: European Conference on Computer Vision, Springer. pp. 37–53.
  * Liu et al. [2021a] Liu, J., Chen, P., Liang, T., Li, Z., Yu, C., Zou, S., Dai, J., Han, J., 2021a.  Li-net: Large-pose identity-preserving face reenactment network, in: 2021 IEEE International Conference on Multimedia and Expo (ICME), IEEE. pp. 1–6.
  * Liu et al. [2019] Liu, M., Ding, Y., Xia, M., Liu, X., Ding, E., Zuo, W., Wen, S., 2019.  Stgan: A unified selective transfer network for arbitrary image attribute editing, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3673–3682.
  * Liu et al. [2021b] Liu, M.Y., Huang, X., Yu, J., Wang, T.C., Mallya, A., 2021b.  Generative adversarial networks for image and video synthesis: Algorithms and applications.  Proceedings of the IEEE 109, 839–862.
  * Liu et al. [2020a] Liu, S., Li, D., Cao, T., Sun, Y., Hu, Y., Ji, J., 2020a.  Gan-based face attribute editing.  IEEE Access 8, 34854–34867.
  * Liu et al. [2017] Liu, S., Sun, Y., Zhu, D., Bao, R., Wang, W., Shu, X., Yan, S., 2017.  Face aging with contextual generative adversarial nets, in: Proceedings of the 25th ACM international conference on Multimedia, pp. 82–90.
  * Liu et al. [2022] Liu, Y., Chen, Y., Bao, L., Sebe, N., Lepri, B., De Nadai, M., 2022.  Isf-gan: An implicit style function for high-resolution image-to-image translation.  IEEE Transactions on Multimedia .
  * Liu et al. [2020b] Liu, Y., De Nadai, M., Cai, D., Li, H., Alameda-Pineda, X., Sebe, N., Lepri, B., 2020b.  Describe what to change: A text-guided unsupervised image-to-image translation approach, in: Proceedings of the 28th ACM International Conference on Multimedia, pp. 1357–1365.
  * Liu et al. [2021c] Liu, Y., Li, Q., Sun, Z., Tan, T., 2021c.  A 3 gan: An attribute-aware attentive generative adversarial network for face aging.  IEEE Transactions on Information Forensics and Security 16, 2776–2790.
  * Liu et al. [2021d] Liu, Y., Sangineto, E., Chen, Y., Bao, L., Zhang, H., Sebe, N., Lepri, B., Wang, W., De Nadai, M., 2021d.  Smoothing the disentangled latent style space for unsupervised image-to-image translation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10785–10794.
  * Liu et al. [2015] Liu, Z., Luo, P., Wang, X., Tang, X., 2015.  Deep learning face attributes in the wild, in: Proceedings of the IEEE international conference on computer vision, pp. 3730–3738.
  * Lu et al. [2021] Lu, Y., Chai, J., Cao, X., 2021.  Live speech portraits: real-time photorealistic talking-head animation.  ACM Transactions on Graphics (TOG) 40, 1–17.
  * Luan et al. [2020] Luan, X., Geng, H., Liu, L., Li, W., Zhao, Y., Ren, M., 2020.  Geometry structure preserving based gan for multi-pose face frontalization and recognition.  IEEE Access .
  * Luo et al. [2021] Luo, H., Nagano, K., Kung, H.W., Xu, Q., Wang, Z., Wei, L., Hu, L., Li, H., 2021.  Normalized avatar synthesis using stylegan and perceptual refinement, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11662–11672.
  * Mao et al. [2019] Mao, W., Lou, B., Yuan, J., 2019.  Tunagan: Interpretable gan for smart editing.  arXiv preprint arXiv:1908.06163 .
  * Mirsky and Lee [2021] Mirsky, Y., Lee, W., 2021.  The creation and detection of deepfakes: A survey.  ACM Computing Surveys (CSUR) 54, 1–41.
  * Moschoglou et al. [2017] Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., Zafeiriou, S., 2017\.  Agedb: the first manually collected, in-the-wild age database, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 51–59.
  * Natsume et al. [2018] Natsume, R., Yatagawa, T., Morishima, S., 2018.  Rsgan: face swapping and editing using face and hair representation in latent spaces, in: ACM SIGGRAPH 2018 Posters, pp. 1–2.
  * Ngo et al. [2020] Ngo, L.M., Karaoglu, S., Gevers, T., et al., 2020.  Unified application of style transfer for face swapping and reenactment, in: Proceedings of the Asian Conference on Computer Vision.
  * Nguyen et al. [2021] Nguyen, T., Tran, A.T., Hoai, M., 2021.  Lipstick ain’t enough: Beyond color matching for in-the-wild makeup transfer, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13305–13314.
  * Nie et al. [2020] Nie, W., Wang, Z., Patel, A.B., Baraniuk, R.G., 2020.  An improved semi-supervised vae for learning disentangled representations.  arXiv preprint arXiv:2006.07460 .
  * Ning et al. [2021] Ning, X., Li, W., Dong, X., Xu, S., Nan, F., Yao, Y., 2021.  Continuous learning of face attribute synthesis, in: 2020 25th International Conference on Pattern Recognition (ICPR), IEEE. pp. 4282–4289.
  * Ning et al. [2020] Ning, X., Xu, S., Li, W., Nie, S., 2020.  Fegan: flexible and efficient face editing with pre-trained generator.  IEEE Access 8, 65340–65350.
  * Nirkin et al. [2019] Nirkin, Y., Keller, Y., Hassner, T., 2019.  Fsgan: Subject agnostic face swapping and reenactment, in: Proceedings of the IEEE international conference on computer vision, pp. 7184–7193.
  * Nitzan et al. [2020] Nitzan, Y., Bermano, A., Li, Y., Cohen-Or, D., 2020\.  Face identity disentanglement via latent space mapping.  ACM Transactions on Graphics (TOG) 39, 1–14.
  * Or-El et al. [2020] Or-El, R., Sengupta, S., Fried, O., Shechtman, E., Kemelmacher-Shlizerman, I., 2020.  Lifespan age transformation synthesis, in: European Conference on Computer Vision, Springer. pp. 739–755.
  * Park et al. [2020] Park, T., Zhu, J.Y., Wang, O., Lu, J., Shechtman, E., Efros, A., Zhang, R., 2020\.  Swapping autoencoder for deep image manipulation.  Advances in Neural Information Processing Systems 33\.
  * Parkhi et al. [2015] Parkhi, O.M., Vedaldi, A., Zisserman, A., 2015.  Deep face recognition .
  * Petrov et al. [2020] Petrov, I., Gao, D., Chervoniy, N., Liu, K., Marangonda, S., Umé, C., Jiang, J., RP, L., Zhang, S., Wu, P., et al., 2020\.  Deepfacelab: A simple, flexible and extensible face swapping framework.  arXiv preprint arXiv:2005.05535 .
  * Pinkney and Adler [2020] Pinkney, J.N., Adler, D., 2020\.  Resolution dependent gan interpolation for controllable image synthesis between domains.  arXiv preprint arXiv:2010.05334 .
  * Pumarola et al. [2018] Pumarola, A., Agudo, A., Martinez, A.M., Sanfeliu, A., Moreno-Noguer, F., 2018.  Ganimation: Anatomically-aware facial animation from a single image, in: Proceedings of the European conference on computer vision (ECCV), pp. 818–833.
  * Ramya et al. [2019] Ramya, Y., Ghosh, S., Vatsa, M., Singh, R., 2019\.  Face sketch colorization via supervised gans, in: 2019 International Conference on Biometrics (ICB), IEEE. pp. 1–6.
  * Ren et al. [2021] Ren, Y., Li, G., Chen, Y., Li, T.H., Liu, S., 2021\.  Pirenderer: Controllable portrait image generation via semantic neural rendering, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 13759–13768.
  * Ricanek and Tesafaye [2006] Ricanek, K., Tesafaye, T., 2006\.  Morph: A longitudinal image database of normal adult age-progression, in: 7th International Conference on Automatic Face and Gesture Recognition (FGR06), IEEE. pp. 341–345.
  * Richardson et al. [2021a] Richardson, E., Alaluf, Y., Patashnik, O., Nitzan, Y., Azar, Y., Shapiro, S., Cohen-Or, D., 2021a.  Encoding in style: a stylegan encoder for image-to-image translation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2287–2296.
  * Richardson et al. [2021b] Richardson, E., Alaluf, Y., Patashnik, O., Nitzan, Y., Azar, Y., Shapiro, S., Cohen-Or, D., 2021b.  Encoding in style: a stylegan encoder for image-to-image translation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2287–2296.
  * Romero et al. [2019] Romero, A., Arbeláez, P., Van Gool, L., Timofte, R., 2019\.  Smit: Stochastic multi-label image-to-image translation, in: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 0–0.
  * Rothe et al. [2018] Rothe, R., Timofte, R., Van Gool, L., 2018.  Deep expectation of real and apparent age from a single image without facial landmarks.  International Journal of Computer Vision 126, 144–157.
  * Salimans et al. [2016] Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X., 2016\.  Improved techniques for training gans, in: Advances in neural information processing systems, pp. 2234–2242.
  * Scherhag et al. [2019] Scherhag, U., Rathgeb, C., Merkle, J., Breithaupt, R., Busch, C., 2019.  Face recognition systems under morphing attacks: A survey.  IEEE Access 7, 23012–23026.
  * Sengupta et al. [2016] Sengupta, S., Chen, J.C., Castillo, C., Patel, V.M., Chellappa, R., Jacobs, D.W., 2016\.  Frontal to profile face verification in the wild, in: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE. pp. 1–9.
  * Sharma et al. [2020] Sharma, N., Sharma, R., Jindal, N., 2020.  An improved technique for face age progression and enhanced super-resolution with generative adversarial networks.  WIRELESS PERSONAL COMMUNICATIONS .
  * Shen et al. [2020] Shen, Y., Yang, C., Tang, X., Zhou, B., 2020.  Interfacegan: Interpreting the disentangled face representation learned by gans.  IEEE transactions on pattern analysis and machine intelligence .
  * Sheng et al. [2020] Sheng, M., Ma, Z., Jia, H., Mao, Q., Dong, M., 2020\.  Face aging with conditional generative adversarial network guided by ranking-cnn, in: 2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), IEEE. pp. 314–319.
  * Shi et al. [2020] Shi, C., Zhang, J., Yao, Y., Sun, Y., Rao, H., Shu, X., 2020.  Can-gan: Conditioned-attention normalized gan for face age synthesis.  Pattern Recognition Letters 138, 520–526.
  * Song et al. [2019] Song, L., Cao, J., Song, L., Hu, Y., He, R., 2019\.  Geometry-aware face completion and editing, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 2506–2513.
  * Song et al. [2020] Song, X., Shao, M., Zuo, W., Li, C., 2020.  Face attribute editing based on generative adversarial networks.  Signal, Image and Video Processing , 1–9.
  * Sun et al. [2020] Sun, P., Li, Y., Qi, H., Lyu, S., 2020.  Landmarkgan: Synthesizing faces from landmarks.  arXiv preprint arXiv:2011.00269 .
  * Sun et al. [2018] Sun, Q., Ma, L., Oh, S.J., Van Gool, L., Schiele, B., Fritz, M., 2018.  Natural and effective obfuscation by head inpainting, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5050–5059.
  * Sun et al. [2021] Sun, R., Huang, C., Zhu, H., Ma, L., 2021.  Mask-aware photorealistic facial attribute manipulation.  Computational Visual Media , 1–12.
  * Tan et al. [2020] Tan, Z., Chai, M., Chen, D., Liao, J., Chu, Q., Yuan, L., Tulyakov, S., Yu, N., 2020.  Michigan: multi-input-conditioned hair image generation for portrait editing.  ACM Transactions on Graphics (TOG) 39, 95–1.
  * Tang et al. [2020] Tang, J., Shao, Z., Ma, L., 2020.  Fine-grained expression manipulation via structured latent space, in: 2020 IEEE International Conference on Multimedia and Expo (ICME), IEEE. pp. 1–6.
  * Tao et al. [2019] Tao, R., Li, Z., Tao, R., Li, B., 2019.  Resattr-gan: Unpaired deep residual attributes learning for multi-domain face image translation.  IEEE Access 7, 132594–132608.
  * Terhörst et al. [2020] Terhörst, P., Fährmann, D., Kolf, J.N., Damer, N., Kirchbuchner, F., Kuijper, A., 2020\.  Maad-face: A massively annotated attribute dataset for face images.  arXiv preprint arXiv:2012.01030 .
  * Tewari et al. [2020a] Tewari, A., Elgharib, M., Bernard, F., Seidel, H.P., Pérez, P., Zollhöfer, M., Theobalt, C., 2020a.  Pie: Portrait image embedding for semantic control.  ACM Transactions on Graphics (TOG) 39, 1–14.
  * Tewari et al. [2020b] Tewari, A., Elgharib, M., Bharaj, G., Bernard, F., Seidel, H.P., Pérez, P., Zollhofer, M., Theobalt, C., 2020b.  Stylerig: Rigging stylegan for 3d control over portrait images, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6142–6151.
  * Tolosana et al. [2020] Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., Ortega-Garcia, J., 2020.  Deepfakes and beyond: A survey of face manipulation and fake detection.  Information Fusion 64, 131–148.
  * Tran et al. [2017] Tran, L., Yin, X., Liu, X., 2017.  Disentangled representation learning gan for pose-invariant face recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1415–1424.
  * Tripathy et al. [2020] Tripathy, S., Kannala, J., Rahtu, E., 2020.  Icface: Interpretable and controllable face reenactment using gans, in: The IEEE Winter Conference on Applications of Computer Vision, pp. 3385–3394.
  * Tripathy et al. [2021] Tripathy, S., Kannala, J., Rahtu, E., 2021.  Facegan: Facial attribute controllable reenactment gan, in: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1329–1338.
  * Tripathy et al. [2022] Tripathy, S., Kannala, J., Rahtu, E., 2022.  Single source one shot reenactment using weighted motion from paired feature points, in: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 2715–2724.
  * Upchurch et al. [2017] Upchurch, P., Gardner, J., Pleiss, G., Pless, R., Snavely, N., Bala, K., Weinberger, K., 2017.  Deep feature interpolation for image content changes, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7064–7073.
  * Usman et al. [2019] Usman, B., Dufour, N., Saenko, K., Bregler, C., 2019\.  Puppetgan: Cross-domain image manipulation by demonstration, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9450–9458.
  * Viazovetskyi et al. [2020] Viazovetskyi, Y., Ivashkin, V., Kashin, E., 2020.  Stylegan2 distillation for feed-forward image manipulation, in: European Conference on Computer Vision, Springer. pp. 170–186.
  * Vijay et al. [2020] Vijay, M., Meghana, M., Aklecha, N., Srinath, R., 2020\.  Dialog driven face construction using gans, in: 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI), IEEE. pp. 647–652.
  * Wang et al. [2021] Wang, C., Chai, M., He, M., Chen, D., Liao, J., 2021\.  Cross-domain and disentangled face manipulation with 3d guidance.  arXiv preprint arXiv:2104.11228 .
  * Wang et al. [2019a] Wang, C.C., Liu, H.H., Pei, S.C., Liu, K.H., Liu, T.J., 2019a.  Face aging on realistic photos by generative adversarial networks, in: 2019 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE. pp. 1–5.
  * Wang et al. [2018a] Wang, F., Chen, L., Li, C., Huang, S., Chen, Y., Qian, C., Loy, C.C., 2018a.  The devil of face recognition is in the noise, in: Proceedings of the European Conference on Computer Vision (ECCV), pp. 765–780.
  * Wang et al. [2019b] Wang, J., Zhang, J., Lu, Z., Shan, S., 2019b.  Dft-net: Disentanglement of face deformation and texture synthesis for expression editing, in: 2019 IEEE International Conference on Image Processing (ICIP), IEEE. pp. 3881–3885.
  * Wang et al. [2017] Wang, K., Gou, C., Duan, Y., Lin, Y., Zheng, X., Wang, F.Y., 2017.  Generative adversarial networks: introduction and outlook.  IEEE/CAA Journal of Automatica Sinica 4, 588–598.
  * Wang et al. [2020a] Wang, L., Chen, W., Yang, W., Bi, F., Yu, F.R., 2020a.  A state-of-the-art review on image synthesis with generative adversarial networks.  IEEE Access 8, 63514–63537.
  * Wang et al. [2018b] Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B., 2018b.  High-resolution image synthesis and semantic manipulation with conditional gans, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8798–8807.
  * Wang et al. [2020b] Wang, X., Wang, K., Lian, S., 2020b.  A survey on face data augmentation for the training of deep neural networks.  Neural computing and applications , 1–29.
  * Wang et al. [2018c] Wang, Y., Wang, S., Qi, G., Tang, J., Li, B., 2018c.  Weakly supervised facial attribute manipulation via deep adversarial network, in: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE. pp. 112–121.
  * Wang et al. [2004] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P., 2004\.  Image quality assessment: from error visibility to structural similarity.  IEEE transactions on image processing 13, 600–612.
  * Wang et al. [2003] Wang, Z., Simoncelli, E.P., Bovik, A.C., 2003.  Multiscale structural similarity for image quality assessment, in: The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, Ieee. pp. 1398–1402.
  * Wei et al. [2020] Wei, Y., Gan, Z., Li, W., Lyu, S., Chang, M.C., Zhang, L., Gao, J., Zhang, P., 2020.  Maggan: High-resolution face attribute editing with mask-guided generative adversarial network.  arXiv preprint arXiv:2010.01424 .
  * Wu et al. [2019a] Wu, P.W., Lin, Y.J., Chang, C.H., Chang, E.Y., Liao, S.W., 2019a.  Relgan: Multi-domain image-to-image translation via relative attributes, in: Proceedings of the IEEE International Conference on Computer Vision, pp. 5914–5922.
  * Wu and Lu [2020] Wu, R., Lu, S., 2020.  Leed: Label-free expression editing via disentanglement, in: European Conference on Computer Vision, Springer. pp. 781–798.
  * Wu et al. [2020] Wu, R., Zhang, G., Lu, S., Chen, T., 2020.  Cascade ef-gan: Progressive facial expression editing with local focuses, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5021–5030.
  * Wu et al. [2018a] Wu, W., Qian, C., Yang, S., Wang, Q., Cai, Y., Zhou, Q., 2018a.  Look at boundary: A boundary-aware face alignment algorithm, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2129–2138.
  * Wu et al. [2018b] Wu, W., Zhang, Y., Li, C., Qian, C., Loy, C.C., 2018b.  Reenactgan: Learning to reenact faces via boundary transfer, in: Proceedings of the European conference on computer vision (ECCV), pp. 603–619.
  * Wu et al. [2019b] Wu, Y., Yang, F., Xu, Y., Ling, H., 2019b.  Privacy-protective-gan for privacy preserving face de-identification.  Journal of Computer Science and Technology 34, 47–60.
  * Wu et al. [2021] Wu, Z., Lischinski, D., Shechtman, E., 2021.  Stylespace analysis: Disentangled controls for stylegan image generation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12863–12872.
  * Xia et al. [2020] Xia, X., Yu, F., Li, N., Qu, Y., Zhang, J., Zhu, C., 2020.  Self-attention-masking semantic decomposition and segmentation for facial attribute manipulation.  IEEE Access 8, 36154–36165.
  * Xiao et al. [2018] Xiao, T., Hong, J., Ma, J., 2018.  Elegant: Exchanging latent encodings with gan for transferring multiple face attributes, in: Proceedings of the European conference on computer vision (ECCV), pp. 168–184.
  * Xu et al. [2021] Xu, S.Z., Huang, H.Z., Zhang, F.L., Zhang, S.H., 2021\.  Faceshapegene: a disentangled shape representation for flexible face image editing.  Graphics and Visual Computing , 200023\.
  * Yan et al. [2016] Yan, X., Yang, J., Sohn, K., Lee, H., 2016.  Attribute2image: Conditional image generation from visual attributes, in: European Conference on Computer Vision, Springer. pp. 776–791.
  * Yang and Lim [2019] Yang, C., Lim, S.N., 2019.  Unconstrained facial expression transfer using style-based generator.  arXiv preprint arXiv:1912.06253 .
  * Yang et al. [2021a] Yang, G., Fei, N., Ding, M., Liu, G., Lu, Z., Xiang, T., 2021a.  L2m-gan: Learning to manipulate latent space semantics for facial attribute editing, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2951–2960.
  * Yang et al. [2021b] Yang, K., Yau, J., Fei-Fei, L., Deng, J., Russakovsky, O., 2021b.  A study of face obfuscation in imagenet.  arXiv preprint arXiv:2103.06191 .
  * Yang et al. [2020] Yang, N., Xu, Y., Zheng, Z., Qi, L., Guo, X., Wang, T., 2020.  Generating and editing arbitrary facial images by learning feature axis.  IEEE Access 8, 135468–135478.
  * Yang et al. [2021c] Yang, S., Xue, H., Ling, J., Song, L., Xie, R., 2021c.  Deep face swapping via cross-identity adversarial training, in: International Conference on Multimedia Modeling, Springer. pp. 74–86.
  * Yao et al. [2021] Yao, X., Puy, G., Newson, A., Gousseau, Y., Hellier, P., 2021\.  High resolution face age editing, in: 2020 25th International Conference on Pattern Recognition (ICPR), IEEE. pp. 8624–8631.
  * Yi et al. [2020a] Yi, R., Ye, Z., Zhang, J., Bao, H., Liu, Y.J., 2020a.  Audio-driven talking face video generation with learning-based personalized head pose.  arXiv preprint arXiv:2002.10137 .
  * Yi et al. [2020b] Yi, Z., Tang, Q., Ramiya Srinivasan, V.S., Xu, Z., 2020b.  Animating through warping: An efficient method for high-quality facial expression animation, in: Proceedings of the 28th ACM International Conference on Multimedia, pp. 1459–1468.
  * Yim et al. [2015] Yim, J., Jung, H., Yoo, B., Choi, C., Park, D., Kim, J., 2015.  Rotating your face using multi-task deep neural network, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 676–684.
  * Yin et al. [2019] Yin, W., Liu, Z., Loy, C.C., 2019.  Instance-level facial attributes transfer with geometry-aware flow, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 9111–9118.
  * Yin et al. [2021] Yin, X., Huang, D., Fu, Z., Wang, Y., Chen, L., 2021\.  Weakly-supervised photo-realistic texture generation for 3d face reconstruction.  arXiv preprint arXiv:2106.08148 .
  * Yin et al. [2020] Yin, Y., Jiang, S., Robinson, J.P., Fu, Y., 2020\.  Dual-attention gan for large-pose face frontalization, in: 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), IEEE. pp. 249–256.
  * Ying et al. [2019] Ying, L., Fan, H., Ni, F., Xiang, J., 2019.  Clsgan: Selective attribute editing based on classification adversarial network [j].  arXiv preprint arXiv:1910.11764 .
  * Yu et al. [2021] Yu, X., Chang, F.J., Chandraker, M., 2021.  Pose-variant 3d facial attribute generation.  US Patent 10,991,145.
  * Zeng et al. [2020] Zeng, X., Pan, Y., Wang, M., Zhang, J., Liu, Y., 2020\.  Realistic face reenactment via self-supervised disentangling of identity and pose, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 12757–12764.
  * Zhang et al. [2018a] Zhang, G., Kan, M., Shan, S., Chen, X., 2018a.  Generative adversarial network with spatial attention for face attribute editing, in: Proceedings of the European conference on computer vision (ECCV), pp. 417–432.
  * Zhang et al. [2019a] Zhang, H., Chen, W., Tian, J., He, H., Jin, Y., 2019a.  Rag: Facial attribute editing by learning residual attributes.  IEEE Access 7, 83266–83276.
  * Zhang et al. [2019b] Zhang, J., Li, A., Liu, Y., Wang, M., 2019b.  Adversarially regularized u-net-based gans for facial attribute modification and generation.  IEEE Access 7, 86453–86462.
  * Zhang et al. [2018b] Zhang, J., Shu, Y., Xu, S., Cao, G., Zhong, F., Liu, M., Qin, X., 2018b.  Sparsely grouped multi-task generative adversarial networks for facial attribute manipulation, in: Proceedings of the 26th ACM international conference on Multimedia, pp. 392–401.
  * Zhang et al. [2020a] Zhang, J., Zeng, X., Wang, M., Pan, Y., Liu, L., Liu, Y., Ding, Y., Fan, C., 2020a.  Freenet: Multi-identity face reenactment, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5326–5335.
  * Zhang et al. [2020b] Zhang, K., Su, Y., Guo, X., Qi, L., Zhao, Z., 2020b.  Mu-gan: Facial attribute editing based on multi-attention mechanism.  IEEE/CAA Journal of Automatica Sinica .
  * Zhang et al. [2018c] Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O., 2018c.  The unreasonable effectiveness of deep features as a perceptual metric, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 586–595.
  * Zhang et al. [2021] Zhang, S., Yuan, J., Liao, M., Zhang, L., 2021.  Text2video: Text-driven talking-head video synthesis with phonetic dictionary.  arXiv preprint arXiv:2104.14631 .
  * Zhang et al. [2020c] Zhang, T., Deng, L., Zhang, L., Dang, X., 2020c.  Deep learning in face synthesis: A survey on deepfakes, in: 2020 IEEE 3rd International Conference on Computer and Communication Engineering Technology (CCET), IEEE. pp. 67–70.
  * Zhang et al. [2018d] Zhang, Z., Chen, X., Wang, B., Hu, G., Zuo, W., Hancock, E.R., 2018d.  Face frontalization using an appearance-flow-based convolutional neural network.  IEEE Transactions on Image Processing 28, 2187–2199.
  * Zhang et al. [2017] Zhang, Z., Song, Y., Qi, H., 2017.  Age progression/regression by conditional adversarial autoencoder, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5810–5818.
  * Zheng et al. [2017] Zheng, T., Deng, W., Hu, J., 2017.  Cross-age lfw: A database for studying cross-age face recognition in unconstrained environments.  arXiv preprint arXiv:1708.08197 .
  * Zheng et al. [2020] Zheng, X., Guo, Y., Huang, H., Li, Y., He, R., 2020\.  A survey of deep facial attribute analysis.  International Journal of Computer Vision , 1–33.
  * Zhou et al. [2021] Zhou, L., Wang, Y., Xiao, B., Xu, Q., 2021.  Dfh-gan: A deep face hashing with generative adversarial network, in: 2020 25th International Conference on Pattern Recognition (ICPR), IEEE. pp. 7012–7019.
  * Zhou et al. [2017] Zhou, S., Xiao, T., Yang, Y., Feng, D., He, Q., He, W., 2017.  Genegan: Learning object transfiguration and attribute subspace from unpaired data.  arXiv preprint arXiv:1705.04932 .
  * Zhu et al. [2019] Zhu, D., Liu, S., Jiang, W., Gao, C., Wu, T., Wang, Q., Guo, G., 2019.  Ugan: Untraceable gan for multi-domain face translation.  arXiv preprint arXiv:1907.11418 .
  * Zhu et al. [2020a] Zhu, H., Huang, Z., Shan, H., Zhang, J., 2020a.  Look globally, age locally: Face aging with an attention mechanism, in: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE. pp. 1963–1967.
  * Zhu et al. [2020b] Zhu, J., Shen, Y., Zhao, D., Zhou, B., 2020b.  In-domain gan inversion for real image editing, in: European conference on computer vision, Springer. pp. 592–608.
  * Zhu et al. [2020c] Zhu, X., Xu, C., Tao, D., 2020c.  Learning disentangled representations with latent variation predictability, in: European Conference on Computer Vision, Springer. pp. 684–700.