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

• URL: http://arxiv.org/abs/2408.01732v1
• Date: Sat, 3 Aug 2024 10:19:38 GMT
• Title: Landmark-guided Diffusion Model for High-fidelity and Temporally Coherent Talking Head Generation
• Authors: Jintao Tan, Xize Cheng, Lingyu Xiong, Lei Zhu, Xiandong Li, Xianjia Wu, Kai Gong, Minglei Li, Yi Cai,
• Abstract summary: We introduce a two-stage diffusion-based model for talking head generation. 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.
• Score: 22.159117464397806
• License: http://creativecommons.org/licenses/by/4.0/
• 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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