Video Echoed in Music: Semantic, Temporal, and Rhythmic Alignment for Video-to-Music Generation
VeM generates music aligned with video semantics, timing, and rhythm, enhancing audiovisual experience.
Key Findings
Methodology
VeM employs hierarchical video parsing with modality-specific encoders and a storyboard-guided cross-attention mechanism (SG-CAtt) to achieve semantic, temporal, and rhythmic alignment. Frame-level transition-beat aligner and adapter (TB-As) dynamically synchronize visual scenes with music beats, ensuring rhythmic precision.
Key Results
- VeM outperforms existing methods in semantic relevance and rhythmic precision, with CLAP score improving to 0.244, LB score reaching 0.930, and TBIoU increasing to 0.364.
- On the new TB-Match dataset, VeM achieves stricter transition-beat synchronization, significantly enhancing video-music generation quality.
- Ablation studies confirm the critical role of SG-CAtt and TB-As in improving semantic and rhythmic alignment of generated music.
Significance
VeM achieves breakthroughs in video-to-music generation by addressing incomplete video detail representation and inadequate rhythm synchronization. Its innovative multimodal alignment mechanism offers new possibilities for advertising, film, and short video production, enhancing audiovisual immersion.
Technical Contribution
VeM introduces hierarchical video parsing as a music conductor, integrating multimodal constraints through SG-CAtt and TB-As. Compared to existing methods, VeM provides new theoretical guarantees and engineering possibilities in semantic, temporal, and rhythmic alignment.
Novelty
VeM is the first to combine hierarchical video parsing with music generation, providing comprehensive alignment from video details to music beats. Compared to existing methods, VeM achieves significant improvements in rhythm synchronization and semantic consistency.
Limitations
- VeM may encounter rhythm synchronization inaccuracies when handling complex video scenes, especially in fast-paced advertisement videos.
- Due to reliance on pre-trained models, VeM may require additional fine-tuning for specific domain videos.
Future Work
Future research can explore VeM's application in real-time video music generation and optimize its performance across different video types. Further improving rhythm synchronization precision is also an important direction.
AI Executive Summary
Current video-to-music generation methods suffer from incomplete video detail representation and inadequate rhythm synchronization. VeM generates music aligned with video semantics, timing, and rhythm through hierarchical video parsing and multimodal alignment mechanisms. Experimental results show that VeM outperforms existing methods in semantic relevance and rhythmic precision, particularly on the new TB-Match dataset. VeM's innovative approach offers new possibilities for advertising, film, and short video production, enhancing audiovisual immersion. However, VeM may encounter rhythm synchronization inaccuracies when handling complex video scenes, and future research can further optimize its performance across different video types.
Deep Analysis
Background
Video-to-music generation aims to create suitable background music for videos, enhancing audiovisual immersion. Existing methods fall short in video detail representation and rhythm synchronization, hindering high-quality music generation.
Core Problem
Existing video-to-music generation methods face bottlenecks in incomplete video detail representation and inadequate rhythm synchronization, making high-quality music generation challenging.
Innovation
VeM achieves semantic, temporal, and rhythmic consistency through hierarchical video parsing and multimodal alignment mechanisms. Its innovative SG-CAtt and TB-As mechanisms significantly enhance the quality of generated music.
Methodology
- �� Hierarchical video parsing provides multimodal video details.
- �� SG-CAtt integrates semantic and temporal cues.
- �� TB-As ensures synchronization of visual scenes with music beats.
Experiments
Experiments conducted on the new TB-Match dataset evaluate VeM's performance in semantic relevance and rhythmic precision, showing its superiority over existing methods.
Results
VeM significantly improves CLAP and LB scores, achieving a TBIoU of 0.364, validating its advantages in semantic and rhythmic alignment.
Applications
VeM can be used in advertising, film, and short video production, providing high-quality background music generation and enhancing audiovisual immersion.
Limitations & Outlook
VeM may encounter rhythm synchronization inaccuracies when handling complex video scenes, and future research can further optimize its performance across different video types.
Plain Language Accessible to non-experts
Imagine watching a movie where VeM acts like a smart music conductor, automatically selecting the most suitable background music based on the movie's plot and visual changes. Whether it's a tense chase scene or a warm family gathering, VeM perfectly matches the music's rhythm and emotion, providing a more immersive audiovisual experience.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a game, and VeM is like a super-smart DJ that automatically plays the perfect background music based on the game's visuals. Whether it's an intense battle or a relaxing exploration, VeM syncs the music with the visuals, making your gaming experience even more exciting!
Glossary
Hierarchical Video Parsing
A technique that decomposes video into multiple levels to extract multimodal details.
Used in VeM's multimodal alignment mechanism.
SG-CAtt
Storyboard-guided cross-attention mechanism for integrating semantic and temporal cues.
Used in VeM for multimodal alignment.
TB-As
Frame-level transition-beat aligner and adapter for synchronizing visual scenes with music beats.
Ensures rhythmic precision in VeM.
CLAP Score
A metric measuring semantic alignment between audio signals and textual descriptions.
Used to evaluate semantic relevance of VeM's generated music.
TBIoU
Transition-Beat Intersection over Union, measuring synchronization of video transitions with music beats.
Used to evaluate rhythmic consistency in VeM.
Open Questions Unanswered questions from this research
- 1 How to apply VeM in real-time video generation and enhance its performance across different video types.
- 2 How to further improve VeM's rhythm synchronization precision, especially in complex video scenes.
Applications
Immediate Applications
Advertising Production
VeM can be used to generate background music for advertisement videos, enhancing audiovisual effects.
Long-term Vision
Film Production
VeM has the potential to be used in film production, automatically generating background music synchronized with the plot, enhancing the viewing experience.
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.