Facial expression transfer and reenactment has been an important research
problem given its applications in face editing, image manipulation, and
fabricated videos generation. We present a novel method for image-based facial
expression transfer, leveraging the recent style-based GAN shown to be very
effective for creating realistic looking images. Given two face images, our
method can create plausible results that combine the appearance of one image
and the expression of the other. To achieve this, we first propose an
optimization procedure based on StyleGAN to infer hierarchical style vector
from an image that disentangle different attributes of the face. We further
introduce a linear combination scheme that fuses the style vectors of the two
given images and generate a new face that combines the expression and
appearance of the inputs. Our method can create high-quality synthesis with
accurate facial reenactment. Unlike many existing methods, we do not rely on
geometry annotations, and can be applied to unconstrained facial images of any
identities without the need for retraining, making it feasible to generate
large-scale expression-transferred results.

提出了一种基于 StyleGAN 模型的面部表情迁移方法，通过优化过程和线性组合方案将两个输入脸部图像的外观属性和表情属性实现合并，从而在不需要几何注释的情况下生成高质量、准确的面部表情合成结果。