Training-Free Multimodal Guidance for Video to Audio Generation

Eleonora Grassucci, Giuliano Galadini, Giordano Cicchetti ...Full author list in the original paper.

Sapienza University of Rome +1 · arXiv

Affiliations: Sapienza University of Rome, Politecnico di Milano

Abstract

Video-to-audio (V2A) generation aims to synthesize realistic and semantically aligned audio from silent videos, with potential applications in video editing, Foley sound design, and assistive multimedia. Although the excellent results, existing approaches either require costly joint training on large-scale paired datasets or rely on pairwise similarities that may fail to capture global multimodal coherence. In this work, we propose a novel training-free multimodal guidance mechanism for V2A diffusion that leverages the volume spanned by the modality embeddings to enforce unified alignment across video, audio, and text. The proposed multimodal diffusion guidance (MDG) provides a lightweight, plug-and-play control signal that can be applied on top of any pretrained audio diffusion model without retraining. Experiments on VGGSound and AudioCaps demonstrate that our MDG consistently improves perceptual quality and multimodal alignment compared to baselines, proving the effectiveness of a joint multimodal guidance for V2A.

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 paper presents a novel training-free multimodal guidance mechanism for video-to-audio generation that significantly improves semantic alignment and audio quality. Its innovative approach, rigorous experimental validation, and potential applications underscore its importance in advancing the field of multimodal machine learning.

Methodology

The proposed methodology introduces a training-free multimodal guidance mechanism for video-to-audio generation that leverages the geometric volume of modality embeddings to enhance semantic alignment across video, audio, and text. This approach is innovative as it circumvents the need for large-scale paired datasets and joint training, which are common limitations in existing methods. The volume-based alignment objective is a significant departure from traditional pairwise similarity measures, potentially leading to more coherent and semantically rich audio outputs. The plug-and-play nature of the method allows it to be integrated with existing pretrained audio diffusion models, enhancing its practicality.

Results & evaluation

The experiments conducted on VGGSound and AudioCaps datasets demonstrate the effectiveness of the proposed method. The authors provide a comprehensive evaluation framework, comparing their approach against established baselines, including Seeing&Hearing and SpecVQGAN. The results show significant improvements in both perceptual quality and multimodal alignment, with quantitative metrics supporting their claims. The evaluation is thorough, addressing both in-domain performance and generalization capabilities, which adds robustness to their findings.

Reproducibility

The paper outlines the implementation details clearly, specifying the use of a pretrained AudioLDM model and the GRAM encoders for modality embeddings. However, the lack of a publicly available code repository or demo URL limits the reproducibility of the results. The authors mention that their method is training-free and can be applied without requiring large datasets, which is a positive aspect for reproducibility.

Limitations

One limitation of the work is the reliance on specific pretrained models for the audio generation process, which may affect the generalizability of the results across different architectures or datasets. Additionally, while the method shows improvements over existing approaches, it would benefit from further exploration of its performance in more diverse and challenging scenarios beyond the tested datasets.

Broader impact

The implications of this research are significant, particularly in fields such as video editing, Foley sound design, and assistive multimedia technologies. By enabling the generation of semantically aligned audio from silent videos, this work could enhance the quality of content creation and accessibility, making it a valuable contribution to the multimedia and machine learning communities. The paper presents a novel training-free multimodal guidance mechanism for video-to-audio generation that significantly improves semantic alignment and audio quality. Its innovative approach, rigorous experimental validation, and potential applications underscore its importance in advancing the field of multimodal machine learning.

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