Face transfer animates the facial performances of the character in the target
video by a source actor. Traditional methods are typically based on face
modeling. We propose an end-to-end face transfer method based on Generative
Adversarial Network. Specifically, we leverage CycleGAN to generate the face
image of the target character with the corresponding head pose and facial
expression of the source. In order to improve the quality of generated videos,
we adopt PatchGAN and explore the effect of different receptive field sizes on
generated images.

提出了一种基于生成对抗网络的端到端人脸转移方法，使用 CycleGAN 生成目标角色的面部图像并探究了 PatchGAN 以及不同感受野大小对生成图像的影响。

基于生成对抗网络的人脸转换

Face Transfer with Generative Adversarial Network

We propose an image-based, facial reenactment system that replaces the face
of an actor in an existing target video with the face of a user from a source
video, while preserving the original target performance. Our system is fully
automatic and does not require a database of source expressions. Instead, it is
able to produce convincing reenactment results from a short source video
captured with an off-the-shelf camera, such as a webcam, where the user
performs arbitrary facial gestures. Our reenactment pipeline is conceived as
part image retrieval and part face transfer: The image retrieval is based on
temporal clustering of target frames and a novel image matching metric that
combines appearance and motion to select candidate frames from the source
video, while the face transfer uses a 2D warping strategy that preserves the
user's identity. Our system excels in simplicity as it does not rely on a 3D
face model, it is robust under head motion and does not require the source and
target performance to be similar. We show convincing reenactment results for
videos that we recorded ourselves and for low-quality footage taken from the
Internet.

提出了一种基于图像的面部再现系统，其可用于将源视频用户的面孔替换为现有目标视频中演员的面孔，并保留原始目标性能，系统具有全自动化和不需要源表情数据库的特点，使用了图像检索和面部转移以产生令人信服的效果。