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Talking face generation has a wide range of potential applications in thefield of virtual digital humans. However, rendering high-fidelity facial videowhile ensuring lip synchronization is still a challenge for existingaudio-driven talking face generation approaches. To address this issue, wepropose HyperLips, a two-stage framework consisting of a hypernetwork forcontrolling lips and a high-resolution decoder for rendering high-fidelityfaces. In the first stage, we construct a base face generation network thatuses the hypernetwork to control the encoding latent code of the visual faceinformation over audio. First, FaceEncoder is used to obtain latent code byextracting features from the visual face information taken from the videosource containing the face frame.Then, HyperConv, which weighting parametersare updated by HyperNet with the audio features as input, will modify thelatent code to synchronize the lip movement with the audio. Finally,FaceDecoder will decode the modified and synchronized latent code into visualface content. In the second stage, we obtain higher quality face videos througha high-resolution decoder. To further improve the quality of face generation,we trained a high-resolution decoder, HRDecoder, using face images and detectedsketches generated from the first stage as input.Extensive quantitative andqualitative experiments show that our method outperforms state-of-the-art workwith more realistic, high-fidelity, and lip synchronization. Project page:https://semchan.github.io/HyperLips Project/

HyperLips: Hyper Control Lips with High Resolution Decoder for Talking Face Generation