LaDTalk: Latent Denoising for Synthesizing Talking Head Videos with High Frequency Details

• URL: http://arxiv.org/abs/2410.00990v1
• Date: Tue, 1 Oct 2024 18:32:02 GMT
• Title: LaDTalk: Latent Denoising for Synthesizing Talking Head Videos with High Frequency Details
• Authors: Jian Yang, Xukun Wang, Wentao Wang, Guoming Li, Qihang Fang, Ruihong Yuan, Tianyang Wang, Jason Zhaoxin Fan,
• Abstract summary: We present an effective post-processing approach to synthesize photo-realistic talking head videos. Specifically, we employ a pretrained Wav2Lip model as our foundation model, leveraging its robust audio-lip alignment capabilities. Results indicate that our method, LaDTalk, achieves new state-of-the-art video quality and out-of-domain lip synchronization performance.
• Score: 14.22392871407274
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Audio-driven talking head generation is a pivotal area within film-making and Virtual Reality. Although existing methods have made significant strides following the end-to-end paradigm, they still encounter challenges in producing videos with high-frequency details due to their limited expressivity in this domain. This limitation has prompted us to explore an effective post-processing approach to synthesize photo-realistic talking head videos. Specifically, we employ a pretrained Wav2Lip model as our foundation model, leveraging its robust audio-lip alignment capabilities. Drawing on the theory of Lipschitz Continuity, we have theoretically established the noise robustness of Vector Quantised Auto Encoders (VQAEs). Our experiments further demonstrate that the high-frequency texture deficiency of the foundation model can be temporally consistently recovered by the Space-Optimised Vector Quantised Auto Encoder (SOVQAE) we introduced, thereby facilitating the creation of realistic talking head videos. We conduct experiments on both the conventional dataset and the High-Frequency TalKing head (HFTK) dataset that we curated. The results indicate that our method, LaDTalk, achieves new state-of-the-art video quality and out-of-domain lip synchronization performance.

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