  In this work, we propose a novel two-stage framework, called FaceShifter, for
high fidelity and occlusion aware face swapping. Unlike many existing face
swapping works that leverage only limited information from the target image
when synthesizing the swapped face, our framework, in its first stage,
generates the swapped face in high-fidelity by exploiting and integrating the
target attributes thoroughly and adaptively. We propose a novel attributes
encoder for extracting multi-level target face attributes, and a new generator
with carefully designed Adaptive Attentional Denormalization (AAD) layers to
adaptively integrate the identity and the attributes for face synthesis. To
address the challenging facial occlusions, we append a second stage consisting
of a novel Heuristic Error Acknowledging Refinement Network (HEAR-Net). It is
trained to recover anomaly regions in a self-supervised way without any manual
annotations. Extensive experiments on wild faces demonstrate that our face
swapping results are not only considerably more perceptually appealing, but
also better identity preserving in comparison to other state-of-the-art
methods.
