The subtleness of human facial expressions and a large degree of variation in
the level of intensity to which a human expresses them is what makes it
challenging to robustly classify and generate images of facial expressions.
Lack of good quality data can hinder the performance of a deep learning model.
In this article, we have proposed a Facial Expression Generation method for
Meta-Humans (FExGAN-Meta) that works robustly with the images of Meta-Humans.
We have prepared a large dataset of facial expressions exhibited by ten
Meta-Humans when placed in a studio environment and then we have evaluated
FExGAN-Meta on the collected images. The results show that FExGAN-Meta robustly
generates and classifies the images of Meta-Humans for the simple as well as
the complex facial expressions.

本文提出了一种针对 Meta-Humans 图像的面部表情生成方法 FExGAN-Meta，通过使用大规模数据集并结合深度学习模型有效地解决了面部表情识别和图像生成中的挑战。

FExGAN-Meta: 基于元人类的面部表情生成

FExGAN-Meta: Facial Expression Generation with Meta Humans

Generative Adversarial Networks (GANs) are currently the method of choice for
generating visual data. Certain GAN architectures and training methods have
demonstrated exceptional performance in generating realistic synthetic images
(in particular, of human faces). However, for 3D object, GANs still fall short
of the success they have had with images. One of the reasons is due to the fact
that so far GANs have been applied as 3D convolutional architectures to
discrete volumetric representations of 3D objects. In this paper, we propose
the first intrinsic GANs architecture operating directly on 3D meshes (named as
MeshGAN). Both quantitative and qualitative results are provided to show that
MeshGAN can be used to generate high-fidelity 3D face with rich identities and
expressions.

本文提出了一种名为 MeshGAN 的新型的生成对抗网络架构，该架构可以直接操作 3D 网格，成功应用于 3D 人脸表情生成。

MeshGAN：非线性三维可塑面部模型

MeshGAN: Non-linear 3D Morphable Models of Faces

Conditional belief networks introduce stochastic binary variables in neural
networks. Contrary to a classical neural network, a belief network can predict
more than the expected value of the output $Y$ given the input $X$. It can
predict a distribution of outputs $Y$ which is useful when an input can admit
multiple outputs whose average is not necessarily a valid answer. Such networks
are particularly relevant to inverse problems such as image prediction for
denoising, or text to speech. However, traditional sigmoid belief networks are
hard to train and are not suited to continuous problems. This work introduces a
new family of networks called linearizing belief nets or LBNs. A LBN decomposes
into a deep linear network where each linear unit can be turned on or off by
non-deterministic binary latent units. It is a universal approximator of
real-valued conditional distributions and can be trained using gradient
descent. Moreover, the linear pathways efficiently propagate continuous
information and they act as multiplicative skip-connections that help
optimization by removing gradient diffusion. This yields a model which trains
efficiently and improves the state-of-the-art on image denoising and facial
expression generation with the Toronto faces dataset.

该论文介绍了一种新的网络类型 —— 线性化置信网（LBN）—— 它是实值条件分布的普遍逼近器，并且能够有效地训练，在图像去噪和面部表情生成等任务中取得了最先进的结果。