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。

FSGANv2：改进的主体无关人脸交换和重现技术

FSGANv2: Improved Subject Agnostic Face Swapping and Reenactment

We present Face Swapping GAN (FSGAN) for face swapping and reenactment.
Unlike previous work, FSGAN is subject agnostic and can be applied to pairs of
faces without requiring training on those faces. To this end, we describe a
number of technical contributions. We derive a novel recurrent neural network
(RNN)-based approach for face reenactment which adjusts for both pose and
expression variations and can be applied to a single image or a video sequence.
For video sequences, we introduce 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
target skin color and lighting conditions. This network uses a novel Poisson
blending loss which combines Poisson optimization with perceptual loss. We
compare our approach to existing state-of-the-art systems and show our results
to be both qualitatively and quantitatively superior.

本文提出了一种基于 GAN 的脸部交换和演绎方法，其中采用了 RNN 和 Delaunay Triangulation 来进行脸部再现，并使用 Poisson blending loss 来实现融合。与现有技术相比，本方法在定性和定量方面均有更好的表现。