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Despite recent advances in syncing lip movements with any audio waves,current methods still struggle to balance generation quality and the model'sgeneralization ability. Previous studies either require long-term data fortraining or produce a similar movement pattern on all subjects with lowquality. In this paper, we propose StyleSync, an effective framework thatenables high-fidelity lip synchronization. We identify that a style-basedgenerator would sufficiently enable such a charming property on both one-shotand few-shot scenarios. Specifically, we design a mask-guided spatialinformation encoding module that preserves the details of the given face. Themouth shapes are accurately modified by audio through modulated convolutions.Moreover, our design also enables personalized lip-sync by introducing stylespace and generator refinement on only limited frames. Thus the identity andtalking style of a target person could be accurately preserved. Extensiveexperiments demonstrate the effectiveness of our method in producinghigh-fidelity results on a variety of scenes. Resources can be found athttps://hangz-nju-cuhk.github.io/projects/StyleSync.

StyleSync: High-Fidelity Generalized and Personalized Lip Sync in Style-based Generator