MuVi: Video-to-Music Generation with Semantic Alignment and Rhythmic Synchronization
Ruiqi Li, Siqi Zheng, Xize Cheng, Ziang Zhang, Shengpeng Ji, Zhou Zhao
2024-10-18
Summary
This paper introduces MuVi, a new system that generates music specifically tailored to match the content and rhythm of a video, enhancing the overall viewing experience.
What's the problem?
Creating music that fits well with video content is challenging because the music needs to reflect the mood and themes of the visuals while also syncing perfectly with the timing and rhythm of what's happening on screen. Traditional methods often struggle to achieve this level of cohesion.
What's the solution?
To solve this problem, the authors developed MuVi, which uses a special visual adaptor to analyze video content and extract important features that relate to both the meaning and timing of the visuals. This information is then used to generate music that matches not only the mood but also the rhythm of the video. They also introduced a training method that helps the system learn how to synchronize music with visual cues effectively. As a result, MuVi can produce high-quality music that aligns seamlessly with videos.
Why it matters?
This research is important because it improves how music can be created for films, animations, and other multimedia projects. By ensuring that the music enhances the visual storytelling, MuVi can make videos more engaging and immersive for viewers. This technology could be particularly useful in industries like entertainment and advertising, where matching audio with visuals is crucial for creating impactful content.
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 https://muvi-v2m.github.io.