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Talking Face Generation (TFG) aims to reconstruct facial movements to achievehigh natural lip movements from audio and facial features that are underpotential connections. Existing TFG methods have made significant advancementsto produce natural and realistic images. However, most work rarely takes visualquality into consideration. It is challenging to ensure lip synchronizationwhile avoiding visual quality degradation in cross-modal generation methods. Toaddress this issue, we propose a universal High-Definition Teeth RestorationNetwork, dubbed HDTR-Net, for arbitrary TFG methods. HDTR-Net can enhance teethregions at an extremely fast speed while maintaining synchronization, andtemporal consistency. In particular, we propose a Fine-Grained Feature Fusion(FGFF) module to effectively capture fine texture feature information aroundteeth and surrounding regions, and use these features to fine-grain the featuremap to enhance the clarity of teeth. Extensive experiments show that our methodcan be adapted to arbitrary TFG methods without suffering from lipsynchronization and frame coherence. Another advantage of HDTR-Net is itsreal-time generation ability. Also under the condition of high-definitionrestoration of talking face video synthesis, its inference speed is $300\%$faster than the current state-of-the-art face restoration based onsuper-resolution.

HDTR-Net: A Real-Time High-Definition Teeth Restoration Network for Arbitrary Talking Face Generation Methods