Video Echoed in Music: Semantic, Temporal, and Rhythmic Alignment for Video-to-Music Generation
Abstract
Video-to-Music generation seeks to generate musically appropriate background music that enhances audiovisual immersion for videos. However, current approaches suffer from two critical limitations: 1) incomplete representation of video details, leading to weak alignment, and 2) inadequate temporal and rhythmic correspondence, particularly in achieving precise beat synchronization. To address the challenges, we propose Video Echoed in Music (VeM), a latent music diffusion that generates high-quality soundtracks with semantic, temporal, and rhythmic alignment for input videos. To capture video details comprehensively, VeM employs a hierarchical video parsing that acts as a music conductor, orchestrating multi-level information across modalities. Modality-specific encoders, coupled with a storyboard-guided cross-attention mechanism (SG-CAtt), integrate semantic cues while maintaining temporal coherence through position and duration encoding. For rhythmic precision, the frame-level transition-beat aligner and adapter (TB-As) dynamically synchronize visual scene transitions with music beats. We further contribute a novel video-music paired dataset sourced from e-commerce advertisements and video-sharing platforms, which imposes stricter transition-beat synchronization requirements. Meanwhile, we introduce novel metrics tailored to the task. Experimental results demonstrate superiority, particularly in semantic relevance and rhythmic precision.
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 approach to video-to-music generation that leverages advanced machine learning techniques to enhance semantic, temporal, and rhythmic alignment. The integration of hierarchical parsing and innovative alignment mechanisms represents a meaningful advancement in the field, addressing critical limitations of existing methods and setting a foundation for future research in multimodal content generation.
Methodology
The proposed methodology, Video Echoed in Music (VeM), introduces a diffusion-based framework that effectively integrates hierarchical video parsing and a storyboard-guided cross-attention mechanism to achieve semantic, temporal, and rhythmic alignment in video-to-music generation. The use of modality-specific encoders and the transition-beat aligner demonstrates a thoughtful approach to addressing the shortcomings of existing methods, particularly in maintaining rhythmic precision and enhancing audiovisual coherence. The hierarchical parsing serves as a robust conductor for the music generation process, allowing for a nuanced understanding of video content that is critical for effective music composition.
Results & evaluation
The experimental evaluation is thorough, utilizing both objective and subjective metrics to assess the performance of VeM against established baselines. The introduction of the TB-Match dataset, specifically designed for this task, adds significant value, as it imposes stricter synchronization requirements that enhance the robustness of the evaluation. The results indicate that VeM outperforms existing methods across various metrics, showcasing improvements in music quality, semantic alignment, temporal synchronization, and rhythmic consistency. The ablation studies further validate the contributions of each component within the framework.
Reproducibility
The paper provides detailed implementation information, including training configurations and the architecture of the models used, which supports reproducibility. However, the reliance on specific pre-trained models and the limited availability of high-quality datasets may pose challenges for full replication of results.
Limitations
The paper acknowledges limitations related to the inherent challenges of achieving perfect alignment between visual transitions and music beats, particularly in scenarios with frequent transitions. Additionally, the focus on specific domains (e-commerce and video-sharing platforms) may limit the generalizability of the findings to other types of video content.
Broader impact
This research has significant implications for various fields, including film, advertising, and gaming, where effective video-to-music generation can enhance user engagement and emotional impact. The methodology could also pave the way for more advanced applications in automated content creation and multimedia editing, potentially transforming how audiovisual content is produced. The paper presents a novel approach to video-to-music generation that leverages advanced machine learning techniques to enhance semantic, temporal, and rhythmic alignment. The integration of hierarchical parsing and innovative alignment mechanisms represents a meaningful advancement in the field, addressing critical limitations of existing methods and setting a foundation for future research in multimodal content generation.
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