We present Face Swapping GAN (FSGAN) for face swapping and reenactment.
Unlike previous work, we offer a subject agnostic swapping scheme that can be
applied to pairs of faces without requiring training on those faces. We derive
a novel iterative deep learning--based approach for face reenactment which
adjusts significant pose and expression variations that can be applied to a
single image or a video sequence. For video sequences, we introduce a
continuous interpolation of the face views based on reenactment, Delaunay
Triangulation, and barycentric coordinates. Occluded face regions are handled
by a face completion network. Finally, we use a face blending network for
seamless blending of the two faces while preserving the target skin color and
lighting conditions. This network uses a novel Poisson blending loss combining
Poisson optimization with a perceptual loss. We compare our approach to
existing state-of-the-art systems and show our results to be both qualitatively
and quantitatively superior. This work describes extensions of the FSGAN
method, proposed in an earlier conference version of our work, as well as
additional experiments and results.

本研究提出了一种基于深度学习的面部交换生成对抗网络（FSGAN），用于面部交换和活体重放，包括一种新颖的迭代深度学习方法，可用于面部活体重放，采用重建三角形和重心坐标的方法连续插值，使用面部补全网络处理被遮挡的面部区域，最终采用一种新型泊松混合损失融合两张面孔并保持目标肤色和照明条件。