This paper presents a novel approach for synthesizing facial affect; either
in terms of the six basic expressions (i.e., anger, disgust, fear, joy, sadness
and surprise), or in terms of valence (i.e., how positive or negative is an
emotion) and arousal (i.e., power of the emotion activation). The proposed
approach accepts the following inputs: i) a neutral 2D image of a person; ii) a
basic facial expression or a pair of valence-arousal (VA) emotional state
descriptors to be generated, or a path of affect in the 2D VA Space to be
generated as an image sequence. In order to synthesize affect in terms of VA,
for this person, $600,000$ frames from the 4DFAB database were annotated. The
affect synthesis is implemented by fitting a 3D Morphable Model on the neutral
image, then deforming the reconstructed face and adding the inputted affect,
and blending the new face with the given affect into the original image.
Qualitative experiments illustrate the generation of realistic images, when the
neutral image is sampled from thirteen well known lab-controlled or in-the-wild
databases, including Aff-Wild, AffectNet, RAF-DB; comparisons with Generative
Adversarial Networks (GANs) show the higher quality achieved by the proposed
approach. Then, quantitative experiments are conducted, in which the
synthesized images are used for data augmentation in training Deep Neural
Networks to perform affect recognition over all databases; greatly improved
performances are achieved when compared with state-of-the-art methods, as well
as with GAN-based data augmentation, in all cases.

本文提出了一种新的方法，用于合成面部情感，接受中性 2D 人脸图像、基本面部表情或 VA 头像建议，以及 2D VA 空间中的路径作为输入，采用 3D 可变模型，对中性人脸图像进行形变和添加颜色，使生成的面孔带有特定情感，用于数据增强和深度神经网络的联调。

深度神经网络增强：生成面孔进行情感分析

Deep Neural Network Augmentation: Generating Faces for Affect Analysis

This paper presents a novel approach for synthesizing facial affect, which is
based on our annotating 600,000 frames of the 4DFAB database in terms of
valence and arousal. The input of this approach is a pair of these emotional
state descriptors and a neutral 2D image of a person to whom the corresponding
affect will be synthesized. Given this target pair, a set of 3D facial meshes
is selected, which is used to build a blendshape model and generate the new
facial affect. To synthesize the affect on the 2D neutral image, 3DMM fitting
is performed and the reconstructed face is deformed to generate the target
facial expressions. Last, the new face is rendered into the original image.
Both qualitative and quantitative experimental studies illustrate the
generation of realistic images, when the neutral image is sampled from a
variety of well known databases, such as the Aff-Wild, AFEW, Multi-PIE,
AFEW-VA, BU-3DFE, Bosphorus.

该研究提出了一种新颖的面部情感合成方法，使用情感状态描述符和中性图像生成逼真的面部表情。