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。