MoDA: Multi-modal Diffusion Architecture for Talking Head Generation

• URL: http://arxiv.org/abs/2507.03256v2
• Date: Wed, 23 Jul 2025 07:07:10 GMT
• Title: MoDA: Multi-modal Diffusion Architecture for Talking Head Generation
• Authors: Xinyang Li, Gen Li, Zhihui Lin, Yichen Qian, GongXin Yao, Weinan Jia, Aowen Wang, Weihua Chen, Fan Wang,
• Abstract summary: Talking head with arbitrary identities and speech audio remains a crucial problem in the realm of the virtual metaverse.<n>MoDA handles these challenges by: 1) defining a joint parameter space that bridges motion generation and neural rendering, and leveraging flow matching to simplify diffusion learning.<n> Experimental results demonstrate that MoDA improves video diversity, realism, and efficiency, making it suitable for real-world applications.
• Score: 18.042826252731714
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Talking head generation with arbitrary identities and speech audio remains a crucial problem in the realm of the virtual metaverse. Recently, diffusion models have become a popular generative technique in this field with their strong generation capabilities. However, several challenges remain for diffusion-based methods: 1) inefficient inference and visual artifacts caused by the implicit latent space of Variational Auto-Encoders (VAE), which complicates the diffusion process; 2) a lack of authentic facial expressions and head movements due to inadequate multi-modal information fusion. In this paper, MoDA handles these challenges by: 1) defining a joint parameter space that bridges motion generation and neural rendering, and leveraging flow matching to simplify diffusion learning; 2) introducing a multi-modal diffusion architecture to model the interaction among noisy motion, audio, and auxiliary conditions, enhancing overall facial expressiveness. In addition, a coarse-to-fine fusion strategy is employed to progressively integrate different modalities, ensuring effective feature fusion. Experimental results demonstrate that MoDA improves video diversity, realism, and efficiency, making it suitable for real-world applications. Project Page: https://lixinyyang.github.io/MoDA.github.io/

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