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Faceshift2/14/2023 ![]() It is trained to recover anomaly regions in a self-supervised way without any manual annotations. ![]() ![]() To address the challenging facial occlusions, we append a second stage consisting of a novel Heuristic Error Acknowledging Refinement Network (HEAR-Net). 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. 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. In this work, we propose a novel two-stage framework, called FaceShifter, for high fidelity and occlusion aware face swapping.
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