Multimodal Music Generation with Explicit Bridges and Retrieval Augmentation

TL;DR

This paper introduces a novel multimodal music generation framework that uses explicit text and music bridges to improve alignment, controllability, and quality across various input modalities like videos, images, and text.

Contribution

It proposes the Visuals Music Bridge (VMB), a new method combining detailed visual-to-text descriptions and targeted music retrieval to enhance multimodal music generation.

Findings

VMB improves music quality and alignment over previous methods.

The framework enables effective controllability in music generation.

Experiments demonstrate versatility across multiple input modalities.

Abstract

Multimodal music generation aims to produce music from diverse input modalities, including text, videos, and images. Existing methods use a common embedding space for multimodal fusion. Despite their effectiveness in other modalities, their application in multimodal music generation faces challenges of data scarcity, weak cross-modal alignment, and limited controllability. This paper addresses these issues by using explicit bridges of text and music for multimodal alignment. We introduce a novel method named Visuals Music Bridge (VMB). Specifically, a Multimodal Music Description Model converts visual inputs into detailed textual descriptions to provide the text bridge; a Dual-track Music Retrieval module that combines broad and targeted retrieval strategies to provide the music bridge and enable user control. Finally, we design an Explicitly Conditioned Music Generation framework to generate music based on the two bridges. We conduct experiments on video-to-music, image-to-music, text-to-music, and controllable music generation tasks, along with experiments on controllability. The results demonstrate that VMB significantly enhances music quality, modality, and customization alignment compared to previous methods. VMB sets a new standard for interpretable and expressive multimodal music generation with applications in various multimedia fields. Demos and code are available at https://github.com/wbs2788/VMB.