# Diff-BGM: A Diffusion Model for Video Background Music Generation

arXiv: https://arxiv.org/abs/2405.11913 · HTML: https://mlsanity.com/paper/2405.11913/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Placed in Audio, speech and music, one of 14 directions in Diffusion models.
- Builds on 1 of its 42 references that are in the map.
- First posted on arXiv 20 May 2024.

## Placed in

- Diffusion models / [Audio, speech and music](https://mlsanity.com/diffusion/audio-speech-and-music/)

## Builds on (most cited first, 1 of 1)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)

## Built on by (most cited first, 23 of 23)

- [VidMuse: A Simple Video-to-Music Generation Framework with Long-Short-Term Modeling](https://mlsanity.com/paper/2406.04321/) (2024)
- [AudioX: A Unified Framework for Anything-to-Audio Generation](https://mlsanity.com/paper/2503.10522/) (2025)
- [MuVi: Video-to-Music Generation with Semantic Alignment and Rhythmic Synchronization](https://mlsanity.com/paper/2410.12957/) (2024)
- [HarmonySet: A Comprehensive Dataset for Understanding Video-Music Semantic Alignment and Temporal Synchronization](https://mlsanity.com/paper/2503.01725/) (2025)
- [FlowRAM: Grounding Flow Matching Policy with Region-Aware Mamba Framework for Robotic Manipulation](https://mlsanity.com/paper/2506.16201/) (2025)
- [Identity-Preserving Text-to-Video Generation via Training-Free Prompt, Image, and Guidance Enhancement](https://mlsanity.com/paper/2509.01362/) (2025)
- [AutoMV: An Automatic Multi-Agent System for Music Video Generation](https://mlsanity.com/paper/2512.12196/) (2025)
- [Multimodal Music Generation with Explicit Bridges and Retrieval Augmentation](https://mlsanity.com/paper/2412.09428/) (2024)
- [FilmComposer: LLM-Driven Music Production for Silent Film Clips](https://mlsanity.com/paper/2503.08147/) (2025)
- [A Comprehensive Survey on Generative AI for Video-to-Music Generation](https://mlsanity.com/paper/2502.12489/) (2025)
- [Vision-to-Music Generation: A Survey](https://mlsanity.com/paper/2503.21254/) (2025)
- [A Survey on Music Generation from Single-Modal, Cross-Modal, and Multi-Modal Perspectives](https://mlsanity.com/paper/2504.00837/) (2025)
- [Extending Visual Dynamics for Video-to-Music Generation](https://mlsanity.com/paper/2504.07594/) (2025)
- [Controllable Video-to-Music Generation with Multiple Time-Varying Conditions](https://mlsanity.com/paper/2507.20627/) (2025)
- [Echoes Over Time: Unlocking Length Generalization in Video-to-Audio Generation Models](https://mlsanity.com/paper/2602.20981/) (2026)
- [V2M-Zero: Zero-Pair Time-Aligned Video-to-Music Generation](https://mlsanity.com/paper/2603.11042/) (2026)
- [MusicAIR: A Multimodal AI Music Generation Framework Powered by an Algorithm-Driven Core](https://mlsanity.com/paper/2511.17323/) (2025)
- [AudioX-Turbo: A Unified Framework for Efficient Anything-to-Audio Generation](https://mlsanity.com/paper/2606.12555/) (2026)
- [Dialogue-Aware Video-to-Music Generation Using Public Domain Film Collections](https://mlsanity.com/paper/2608.11576/) (2026)
- [Visually Grounded Narratives: Reducing Cognitive Burden in Researcher-Participant Interaction](https://mlsanity.com/paper/2509.00381/) (2025)
- [SyncTrack: Rhythmic Stability and Synchronization in Multi-Track Music Generation](https://mlsanity.com/paper/2603.01101/) (2026)
- [JenBridge: Adaptive Long-Form Video Soundtracking across Scene Transitions](https://mlsanity.com/paper/2606.01703/) (2026)
- [Self-Representation Alignment in Diffusion Models for Video Background Music Generation](https://mlsanity.com/paper/2610.15575/) (2026)

## Abstract (arXiv)

When editing a video, a piece of attractive background music is indispensable. However, video background music generation tasks face several challenges, for example, the lack of suitable training datasets, and the difficulties in flexibly controlling the music generation process and sequentially aligning the video and music. In this work, we first propose a high-quality music-video dataset BGM909 with detailed annotation and shot detection to provide multi-modal information about the video and music. We then present evaluation metrics to assess music quality, including music diversity and alignment between music and video with retrieval precision metrics. Finally, we propose the Diff-BGM framework to automatically generate the background music for a given video, which uses different signals to control different aspects of the music during the generation process, i.e., uses dynamic video features to control music rhythm and semantic features to control the melody and atmosphere. We propose to align the video and music sequentially by introducing a segment-aware cross-attention layer. Experiments verify the effectiveness of our proposed method. The code and models are available at https://github.com/sizhelee/Diff-BGM.