Model-Guided Dual-Role Alignment for High-Fidelity Open-Domain Video-to-Audio Generation
Abstract
We present MGAudio, a novel flow-based framework for open-domain video-to-audio generation, which introduces model-guided dual-role alignment as a central design principle. Unlike prior approaches that rely on classifier-based or classifier-free guidance, MGAudio enables the generative model to guide itself through a dedicated training objective designed for video-conditioned audio generation. The framework integrates three main components: (1) a scalable flow-based Transformer model, (2) a dual-role alignment mechanism where the audio-visual encoder serves both as a conditioning module and as a feature aligner to improve generation quality, and (3) a model-guided objective that enhances cross-modal coherence and audio realism. MGAudio achieves state-of-the-art performance on VGGSound, reducing FAD to 0.40, substantially surpassing the best classifier-free guidance baselines, and consistently outperforms existing methods across FD, IS, and alignment metrics. It also generalizes well to the challenging UnAV-100 benchmark. These results highlight model-guided dual-role alignment as a powerful and scalable paradigm for conditional video-to-audio generation. Code is available at: https://github.com/pantheon5100/mgaudio
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
MGAudio represents a significant advancement in the field of audio generation, introducing a novel model-guided framework that achieves state-of-the-art performance while addressing key challenges in video-to-audio synthesis. The methodology is robust, and the experimental results validate its effectiveness, making it a valuable contribution to the machine learning community.
Methodology
The paper introduces MGAudio, a novel framework for video-to-audio generation that leverages model-guided dual-role alignment. The methodology is well-structured, incorporating a scalable flow-based Transformer model, a dual-role audio-visual encoder, and a model-guided objective. This approach effectively addresses the limitations of existing classifier-free guidance methods by providing direct model-based supervision, enhancing both training efficiency and audio generation quality. The dual-role alignment mechanism is particularly innovative, allowing the audio-visual encoder to serve dual functions that improve feature integration and generation coherence.
Results & evaluation
The experiments are comprehensive, demonstrating the effectiveness of MGAudio on the VGGSound and UnAV-100 benchmarks. The reported results, including a Fréchet Audio Distance (FAD) of 0.40, showcase significant improvements over previous state-of-the-art methods. The paper includes ablation studies that validate the contributions of each component, providing a thorough analysis of the model's performance across various metrics. The generalization capabilities of the model are also highlighted, indicating robustness across different datasets.
Reproducibility
The paper provides sufficient implementation details, including training configurations, model architecture, and evaluation metrics, which facilitate reproducibility. The authors have made the code publicly available, further supporting the reproducibility of their results. However, more detailed descriptions of hyperparameter tuning and specific experimental setups could enhance reproducibility.
Limitations
The paper acknowledges limitations in generating complex audio such as human vocalizations, indicating that the model may struggle with subtle visual cues. Additionally, the reliance on iterative sampling processes could hinder inference speed, which is a common challenge in generative models. The authors suggest future work could focus on improving efficiency and addressing these limitations.
Broader impact
The proposed framework has significant implications for various applications, including film production, video editing, and content creation, where realistic audio generation is crucial for enhancing viewer experience. The model's ability to generate coherent audio from video inputs could revolutionize workflows in multimedia production, making it more efficient and accessible. MGAudio represents a significant advancement in the field of audio generation, introducing a novel model-guided framework that achieves state-of-the-art performance while addressing key challenges in video-to-audio synthesis. The methodology is robust, and the experimental results validate its effectiveness, making it a valuable contribution to the machine learning community.
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