The paper proposes a novel generative adversarial network for one-shot face
reenactment, which can animate a single face image to a different
pose-and-expression (provided by a driving image) while keeping its original
appearance. The core of our network is a novel mechanism called appearance
adaptive normalization, which can effectively integrate the appearance
information from the input image into our face generator by modulating the
feature maps of the generator using the learned adaptive parameters.
Furthermore, we specially design a local net to reenact the local facial
components (i.e., eyes, nose and mouth) first, which is a much easier task for
the network to learn and can in turn provide explicit anchors to guide our face
generator to learn the global appearance and pose-and-expression. Extensive
quantitative and qualitative experiments demonstrate the significant efficacy
of our model compared with prior one-shot methods.

本文提出了一种新颖的生成对抗网络，用于单次面部再现，能够在保持面部原始外观的同时对单个面部图像进行不同的姿势和表情动画效果。作者们设计了外观自适应归一化机制和本地网络结构，用于集成外观信息和优化脸部生成器，实现更加精准的面部再现表达。实验结果表明，该模型在单次方法中取得了显著的效果提升。