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arXiv · 2408.01732

Landmark-guided Diffusion Model for High-fidelity and Temporally Coherent Talking Head Generation

Jintao Tan·Xize Cheng·Lingyu Xiong·Lei Zhu·Xiandong Li·Xianjia Wu·Kai Gong·Minglei Li·Yi Cai
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Abstract

Audio-driven talking head generation is a significant and challenging task applicable to various fields such as virtual avatars, film production, and online conferences. However, the existing GAN-based models emphasize generating well-synchronized lip shapes but overlook the visual quality of generated frames, while diffusion-based models prioritize generating high-quality frames but neglect lip shape matching, resulting in jittery mouth movements. To address the aforementioned problems, we introduce a two-stage diffusion-based model. The first stage involves generating synchronized facial landmarks based on the given speech. In the second stage, these generated landmarks serve as a condition in the denoising process, aiming to optimize mouth jitter issues and generate high-fidelity, well-synchronized, and temporally coherent talking head videos. Extensive experiments demonstrate that our model yields the best performance.

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Jintao Tan, Xize Cheng, Lingyu Xiong, Lei Zhu, Xiandong Li, Xianjia Wu, Kai Gong, Minglei Li, Yi Cai. 2024-08-03. Landmark-guided Diffusion Model for High-fidelity and Temporally Coherent Talking Head Generation. https://arxiv.org/abs/2408.01732

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