HDTR-Net: A Real-Time High-Definition Teeth Restoration Network for
  Arbitrary Talking Face Generation Methods
Yongyuan Li Xiuyuan Qin Chao Liang Mingqiang Wei
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.