This paper presents Generative Adversarial Talking Head (GATH), a novel deep
generative neural network that enables fully automatic facial expression
synthesis of an arbitrary portrait with continuous action unit (AU)
coefficients. Specifically, our model directly manipulates image pixels to make
the unseen subject in the still photo express various emotions controlled by
values of facial AU coefficients, while maintaining her personal
characteristics, such as facial geometry, skin color and hair style, as well as
the original surrounding background. In contrast to prior work, GATH is purely
data-driven and it requires neither a statistical face model nor image
processing tricks to enact facial deformations. Additionally, our model is
trained from unpaired data, where the input image, with its auxiliary identity
label taken from abundance of still photos in the wild, and the target frame
are from different persons. In order to effectively learn such model, we
propose a novel weakly supervised adversarial learning framework that consists
of a generator, a discriminator, a classifier and an action unit estimator. Our
work gives rise to template-and-target-free expression editing, where still
faces can be effortlessly animated with arbitrary AU coefficients provided by
the user.

本文提出了一种名为 GATH 的新型深度生成神经网络，利用动作单元系数实现任意肖像的完全自动面部表情合成。与以往工作不同，GATH 仅仅需要数据驱动，无需人脸模型或图像处理技巧实现面部变形。此外，我们还提出了一种弱监督对抗学习框架，通过生成器、判别器、分类器和动作单元估计器有效地学习该模型，从而实现了无模板目标的表情编辑。