Facial expression synthesis or editing has recently received increasing
attention in the field of affective computing and facial expression modeling.
However, most existing facial expression synthesis works are limited in paired
training data, low resolution, identity information damaging, and so on. To
address those limitations, this paper introduces a novel Action Unit (AU) level
facial expression synthesis method called Local Attentive Conditional
Generative Adversarial Network (LAC-GAN) based on face action units
annotations. Given desired AU labels, LAC-GAN utilizes local AU regional rules
to control the status of each AU and attentive mechanism to combine several of
them into the whole photo-realistic facial expressions or arbitrary facial
expressions. In addition, unpaired training data is utilized in our proposed
method to train the manipulation module with the corresponding AU labels, which
learns a mapping between a facial expression manifold. Extensive qualitative
and quantitative evaluations are conducted on the commonly used BP4D dataset to
verify the effectiveness of our proposed AU synthesis method.

本文提出了一种面部动作单元（AU）级别的面部表情合成方法，使用基于 AU 注释的本地注意条件生成对抗网络（LAC-GAN）控制 AU 状态，并使用非配对训练数据训练操纵模块， 在常用的 BP4D 数据集上进行定量和定性评估以验证该方法的有效性。

基于区域的面部动作单元对抗合成

Region Based Adversarial Synthesis of Facial Action Units

This paper proposes a supervised learning approach to jointly perform facial
Action Unit (AU) localisation and intensity estimation. Contrary to previous
works that try to learn an unsupervised representation of the Action Unit
regions, we propose to directly and jointly estimate all AU intensities through
heatmap regression, along with the location in the face where they cause
visible changes. Our approach aims to learn a pixel-wise regression function
returning a score per AU, which indicates an AU intensity at a given spatial
location. Heatmap regression then generates an image, or channel, per AU, in
which each pixel indicates the corresponding AU intensity. To generate the
ground-truth heatmaps for a target AU, the facial landmarks are first
estimated, and a 2D Gaussian is drawn around the points where the AU is known
to cause changes. The amplitude and size of the Gaussian is determined by the
intensity of the AU. We show that using a single Hourglass network suffices to
attain new state of the art results, demonstrating the effectiveness of such a
simple approach. The use of heatmap regression allows learning of a shared
representation between AUs without the need to rely on latent representations,
as these are implicitly learned from the data. We validate the proposed
approach on the BP4D dataset, showing a modest improvement on recent, complex,
techniques, as well as robustness against misalignment errors. Code for testing
and models will be available to download from
this https URL

本论文提出了一种监督学习方法，同时实现面部动作单位（AU）的本地化和强度估计，通过热图回归直接和联合估计所有 AU 强度和其可能导致可见变化的面部位置，旨在学习每 AU 的每个空间位置的得分。在 BP4D 数据集中，该方法取得了最新技术水平，同时对位移误差具有稳健性。