READ: Real-time and Efficient Asynchronous Diffusion for Audio-driven Talking Head Generation

• URL: http://arxiv.org/abs/2508.03457v2
• Date: Wed, 06 Aug 2025 11:50:17 GMT
• Title: READ: Real-time and Efficient Asynchronous Diffusion for Audio-driven Talking Head Generation
• Authors: Haotian Wang, Yuzhe Weng, Jun Du, Haoran Xu, Xiaoyan Wu, Shan He, Bing Yin, Cong Liu, Jianqing Gao, Qingfeng Liu,
• Abstract summary: We propose READ, the first real-time diffusion-transformer-based talking head generation framework.<n>Our approach first learns highly compressed video latent space via a VAE, significantly reducing the token count to speech generation.<n>We show that READ outperforms state-of-the-art methods by generating competitive talking head videos with significantly reduced runtime.
• Score: 55.58089937219475
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The introduction of diffusion models has brought significant advances to the field of audio-driven talking head generation. However, the extremely slow inference speed severely limits the practical implementation of diffusion-based talking head generation models. In this study, we propose READ, the first real-time diffusion-transformer-based talking head generation framework. Our approach first learns a spatiotemporal highly compressed video latent space via a temporal VAE, significantly reducing the token count to accelerate generation. To achieve better audio-visual alignment within this compressed latent space, a pre-trained Speech Autoencoder (SpeechAE) is proposed to generate temporally compressed speech latent codes corresponding to the video latent space. These latent representations are then modeled by a carefully designed Audio-to-Video Diffusion Transformer (A2V-DiT) backbone for efficient talking head synthesis. Furthermore, to ensure temporal consistency and accelerated inference in extended generation, we propose a novel asynchronous noise scheduler (ANS) for both the training and inference process of our framework. The ANS leverages asynchronous add-noise and asynchronous motion-guided generation in the latent space, ensuring consistency in generated video clips. Experimental results demonstrate that READ outperforms state-of-the-art methods by generating competitive talking head videos with significantly reduced runtime, achieving an optimal balance between quality and speed while maintaining robust metric stability in long-time generation.

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