High Fidelity Face Swapping via Semantics Disentanglement and Structure Enhancement

In this paper, we propose a novel Semantics and Structure-aware face Swapping framework (S2Swap) that exploits semantics disentanglement and structure enhancement for high fidelity face generation. Different from previous methods that either 1) suffer from degraded generation fidelity due to insufficient identity-attributes disentanglement or 2) neglect the importance of structure information for identity consistency, our approach can achieve local facial semantics disentanglement beyond global identity while boosting identity consistency through structure enhancement. Specifically, to achieve identity-attributes disentanglement, our S2Swap is designed from global-local perspectives. Firstly, an Oriented Identity Transfer module is proposed to globally disentangle target identity and attributes under global identity semantics prior. Such global disentanglement enables source identity transfer to the individual target identity. Secondly, a Local Semantics Disentanglement module is devised to disentangle local identity and identity-irrelevant facial semantics, providing local semantic compensation for the global counterpart. Moreover, to boost identity consistency, a Structure-Aware Head Modeling module is introduced to provide the desired face structure enhancement through an intuitive face sketch. Finally, considering the identity-attributes trade-off, we adaptively integrate semantics and structure information in a self-learning manner. Extensive experiments qualitatively and quantitatively show that our method outperforms SOTA face swapping methods in terms of both identity transfer and attribute preservation.