Video Echoed in Music: Semantic, Temporal, and Rhythmic Alignment for Video-to-Music Generation

TL;DR

This paper introduces VeM, a novel video-to-music generation method that achieves semantic, temporal, and rhythmic alignment by leveraging hierarchical video parsing, cross-attention mechanisms, and beat synchronization techniques, supported by a new dataset and metrics.

Contribution

VeM is the first approach to comprehensively align semantic content, timing, and rhythm in video-to-music generation using hierarchical parsing and specialized synchronization modules.

Findings

VeM outperforms existing methods in semantic relevance.

VeM achieves high rhythmic synchronization accuracy.

The new dataset enables stricter evaluation of beat alignment.

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.