Diff-V2M: A Hierarchical Conditional Diffusion Model with Explicit Rhythmic Modeling for Video-to-Music Generation
Abstract
Video-to-music (V2M) generation aims to create music that aligns with visual content. However, two main challenges persist in existing methods: (1) the lack of explicit rhythm modeling hinders audiovisual temporal alignments; (2) effectively integrating various visual features to condition music generation remains non-trivial. To address these issues, we propose Diff-V2M, a general V2M framework based on a hierarchical conditional diffusion model, comprising two core components: visual feature extraction and conditional music generation. For rhythm modeling, we begin by evaluating several rhythmic representations, including low-resolution mel-spectrograms, tempograms, and onset detection functions (ODF), and devise a rhythmic predictor to infer them directly from videos. To ensure contextual and affective coherence, we also extract semantic and emotional features. All features are incorporated into the generator via a hierarchical cross-attention mechanism, where emotional features shape the affective tone via the first layer, while semantic and rhythmic features are fused in the second cross-attention layer. To enhance feature integration, we introduce timestep-aware fusion strategies, including feature-wise linear modulation (FiLM) and weighted fusion, allowing the model to adaptively balance semantic and rhythmic cues throughout the diffusion process. Extensive experiments identify low-resolution ODF as a more effective signal for modeling musical rhythm and demonstrate that Diff-V2M outperforms existing models on both in-domain and out-of-domain datasets, achieving state-of-the-art performance in terms of objective metrics and subjective comparisons. Demo and code are available at https://Tayjsl97.github.io/Diff-V2M-Demo/.
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 Diff-V2M, a hierarchical conditional diffusion model that effectively addresses the challenges of video-to-music generation by incorporating explicit rhythm modeling and a sophisticated feature integration strategy. This work significantly advances the field of audiovisual content generation, providing a robust framework that outperforms existing methods and opens avenues for future research.
Methodology
The proposed methodology in Diff-V2M is innovative, leveraging a hierarchical conditional diffusion model to address the challenges of video-to-music generation. The introduction of a rhythmic predictor that infers rhythmic representations directly from video content is a significant advancement, as it allows for explicit rhythm modeling, which has been a gap in previous works. The use of multiple visual features (emotional, semantic, and rhythmic) integrated through a hierarchical cross-attention mechanism is a robust approach that enhances the model's ability to generate coherent music aligned with video content. The timestep-aware fusion strategies (FiLM and weighted fusion) further demonstrate a thoughtful design to adaptively balance the contributions of different features, enhancing the model's performance.
Results & evaluation
The experimental evaluation is comprehensive, utilizing both in-domain and out-of-domain datasets to assess the model's performance. The paper reports extensive quantitative results that show Diff-V2M outperforms existing methods in various metrics, including Frechet Audio Distance and subjective evaluations. The use of ablation studies to analyze the impact of different components of the model strengthens the findings, providing insights into the importance of rhythmic and emotional features in music generation.
Reproducibility
The paper includes detailed implementation details, including the architecture of the model, training strategies, and evaluation metrics, which contribute to reproducibility. The availability of a demo and code repository enhances the potential for other researchers to replicate the results. However, the actual code repository is not provided, which could limit reproducibility for some aspects.
Limitations
The paper acknowledges limitations, such as the reliance on scene cuts and inter-frame differences, which may overlook subtle motion cues in human-centric videos. Additionally, the model does not offer explicit control over musical attributes like genre and emotion, which could restrict its adaptability in certain contexts. These limitations highlight areas for future research and improvement.
Broader impact
The implications of this research are significant, particularly in the context of personalized audiovisual content creation, which is increasingly relevant in the age of social media and video platforms. The ability to generate music that aligns with visual content can enhance user experiences in various applications, including video editing, gaming, and interactive media. The advancements in rhythm modeling and feature integration could also inspire further research in multimodal generative models. The main contribution of this paper is the introduction of Diff-V2M, a hierarchical conditional diffusion model that effectively addresses the challenges of video-to-music generation by incorporating explicit rhythm modeling and a sophisticated feature integration strategy. This work significantly advances the field of audiovisual content generation, providing a robust framework that outperforms existing methods and opens avenues for future research.
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