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This work proposes a novel face-swapping framework FlowFace++, utilizingexplicit semantic flow supervision and end-to-end architecture to facilitateshape-aware face-swapping. Specifically, our work pretrains a facial shapediscriminator to supervise the face swapping network. The discriminator isshape-aware and relies on a semantic flow-guided operation to explicitlycalculate the shape discrepancies between the target and source faces, thusoptimizing the face swapping network to generate highly realistic results. Theface swapping network is a stack of a pre-trained face-masked autoencoder(MAE), a cross-attention fusion module, and a convolutional decoder. The MAEprovides a fine-grained facial image representation space, which is unified forthe target and source faces and thus facilitates final realistic results. Thecross-attention fusion module carries out the source-to-target face swapping ina fine-grained latent space while preserving other attributes of the targetimage (e.g. expression, head pose, hair, background, illumination, etc).Lastly, the convolutional decoder further synthesizes the swapping resultsaccording to the face-swapping latent embedding from the cross-attention fusionmodule. Extensive quantitative and qualitative experiments on in-the-wild facesdemonstrate that our FlowFace++ outperforms the state-of-the-art significantly,particularly while the source face is obstructed by uneven lighting or angleoffset.

FlowFace++: Explicit Semantic Flow-supervised End-to-End Face Swapping