Multimodal Music Generation with Explicit Bridges and Retrieval
  Augmentation
Authors: name: Baisen Wang, name: Le Zhuo, name: Zhaokai Wang, name: Chenxi Bao, name: Wu Chengjing, name: Xuecheng Nie, name: Jiao Dai, name: Jizhong Han, name: Yue Liao, name: Si Liu

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.