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

提出了一个名为 HDTR-Net 的通用高清修复网络，用于任意对话生成方法，可以在保持同步和时序一致性的同时，以极快的速度增强牙齿区域。实验证明，该方法适用于任意对话生成方法，同时能够实时生成高清对话视频，比基于超分辨率的当前先进脸部修复方法快 300%。