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In this work, we propose a semantic flow-guided two-stage framework forshape-aware face swapping, namely FlowFace. Unlike most previous methods thatfocus on transferring the source inner facial features but neglect facialcontours, our FlowFace can transfer both of them to a target face, thus leadingto more realistic face swapping. Concretely, our FlowFace consists of a facereshaping network and a face swapping network. The face reshaping networkaddresses the shape outline differences between the source and target faces. Itfirst estimates a semantic flow (i.e., face shape differences) between thesource and the target face, and then explicitly warps the target face shapewith the estimated semantic flow. After reshaping, the face swapping networkgenerates inner facial features that exhibit the identity of the source face.We employ a pre-trained face masked autoencoder (MAE) to extract facialfeatures from both the source face and the target face. In contrast to previousmethods that use identity embedding to preserve identity information, thefeatures extracted by our encoder can better capture facial appearances andidentity information. Then, we develop a cross-attention fusion module toadaptively fuse inner facial features from the source face with the targetfacial attributes, thus leading to better identity preservation. Extensivequantitative and qualitative experiments on in-the-wild faces demonstrate thatour FlowFace outperforms the state-of-the-art significantly.

FlowFace: Semantic Flow-guided Shape-aware Face Swapping