EARTalking: End-to-end GPT-style Autoregressive Talking Head Synthesis with Frame-wise Control
Abstract
Audio-driven talking head generation aims to create vivid and realistic videos from a static portrait and speech. Existing AR-based methods rely on intermediate facial representations, which limit their expressiveness and realism. Meanwhile, diffusion-based methods generate clip-by-clip, lacking fine-grained control and causing inherent latency due to overall denoising across the window. To address these limitations, we propose EARTalking, a novel end-to-end, GPT-style autoregressive model for interactive audio-driven talking head generation. Our method introduces a novel frame-by-frame, in-context, audio-driven streaming generation paradigm. For inherently supporting variable-length video generation with identity consistency, we propose the Sink Frame Window Attention (SFA) mechanism. Furthermore, to avoid the complex, separate networks that prior works required for diverse control signals, we propose a streaming Frame Condition In-Context (FCIC) scheme. This scheme efficiently injects diverse control signals in a streaming, in-context manner, enabling interactive control at every frame and at arbitrary moments. Experiments demonstrate that EARTalking outperforms existing autoregressive methods and achieves performance comparable to diffusion-based methods. Our work demonstrates the feasibility of in-context streaming autoregressive control, unlocking a scalable direction for flexible, efficient generation. The code will be released for reproducibility.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of EARTalking, a novel autoregressive model for audio-driven talking head generation that allows for interactive control and variable-length video synthesis. The comprehensive analysis highlights the innovative methodology, strong experimental results, and significant implications for the field of machine learning and multimedia synthesis.
Methodology
The paper introduces a novel end-to-end autoregressive model for talking head generation, addressing limitations of existing methods by proposing the Sink Frame Window Attention (SFA) mechanism and the Frame Condition In-Context (FCIC) scheme. The SFA mechanism allows for variable-length video generation while maintaining identity consistency, which is crucial for realistic video synthesis. The FCIC scheme enables efficient integration of diverse control signals, enhancing the interactivity of the generated videos. The methodology is well-structured and innovative, leveraging the strengths of autoregressive models while mitigating their common drawbacks.
Results & evaluation
The experiments conducted demonstrate that EARTalking outperforms existing autoregressive methods and achieves performance on par with diffusion-based methods. The evaluation metrics used are appropriate, and the results are clearly presented, showcasing the advantages of the proposed method in terms of expressiveness and realism. However, specific metrics for quality evaluation, such as subjective assessments or objective scores, could have been elaborated upon to strengthen the findings.
Reproducibility
The authors mention that the code will be released for reproducibility, which is a positive aspect. However, the paper lacks detailed implementation specifics, such as hyperparameters, training datasets, and computational resources used, which are essential for other researchers to replicate the results effectively.
Limitations
One limitation is the reliance on the autoregressive framework, which may still introduce latency issues compared to non-autoregressive methods. Additionally, while the paper claims to achieve high realism and expressiveness, further qualitative assessments from human evaluators would provide a more comprehensive understanding of the model's performance.
Broader impact
The potential applications of EARTalking are significant, ranging from entertainment and gaming to virtual communication and education. By enabling realistic talking head synthesis, this work could enhance user engagement in various multimedia applications. The implications for ethical considerations in the use of such technology, particularly regarding deepfakes and misinformation, should also be acknowledged. The main contribution of this paper is the introduction of EARTalking, a novel autoregressive model for audio-driven talking head generation that allows for interactive control and variable-length video synthesis. The comprehensive analysis highlights the innovative methodology, strong experimental results, and significant implications for the field of machine learning and multimedia synthesis.
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