MuVi: Video-to-Music Generation with Semantic Alignment and Rhythmic Synchronization

Ruiqi Li, Siqi Zheng, Xize Cheng, Ziang Zhang, Shengpeng Ji, Zhou Zhao

Keywords: Video-to-music generation, music generation

TL;DR: This paper proposes a novel approach to generate music conditioned on video, focusing on the semantic alignment and rhythmic synchronization between music and video.

Abstract: Generating music that aligns with the visual content of a video has been a challenging task, as it requires a deep understanding of visual semantics and involves generating music whose melody, rhythm, and dynamics harmonize with the visual narratives. This paper presents MuVi, a novel framework that effectively addresses these challenges to enhance the cohesion and immersive experience of audio-visual content. MuVi analyzes video content through a specially designed visual adaptor to extract contextually and temporally relevant features. These features are used to generate music that not only matches the video’s mood and theme but also its rhythm and pacing. We also introduce a contrastive music-visual pre-training scheme to ensure synchronization, based on the periodicity nature of music phrases. In addition, we demonstrate that our flow-matching-based music generator has in-context learning ability, allowing us to control the style and genre of the generated music. Experimental results show that MuVi demonstrates superior performance in both audio quality and temporal synchronization. The generated music video samples are available at muvi-v2m.github.io.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 10758