Millions of images of human faces are captured every single day; but these
photographs portray the likeness of an individual with a fixed pose,
expression, and appearance. Portrait image animation enables the post-capture
adjustment of these attributes from a single image while maintaining a
photorealistic reconstruction of the subject's likeness or identity. Still,
current methods for portrait image animation are typically based on 2D warping
operations or manipulations of a 2D generative adversarial network (GAN) and
lack explicit mechanisms to enforce multi-view consistency. Thus these methods
may significantly alter the identity of the subject, especially when the
viewpoint relative to the camera is changed. In this work, we leverage newly
developed 3D GANs, which allow explicit control over the pose of the image
subject with multi-view consistency. We propose a supervision strategy to
flexibly manipulate expressions with 3D morphable models, and we show that the
proposed method also supports editing appearance attributes, such as age or
hairstyle, by interpolating within the latent space of the GAN. The proposed
technique for portrait image animation outperforms previous methods in terms of
image quality, identity preservation, and pose transfer while also supporting
attribute editing.

本文提出了一种基于新开发的三维生成对抗网络（GANs）的画像图像动画的技术，该技术允许对图像主体的姿态进行明确控制，并展示了该方法在图像质量，身份保护和姿态转移等方面胜过先前的方法，同时支持属性修改。