# VidMuse: A Simple Video-to-Music Generation Framework with Long-Short-Term Modeling

arXiv: https://arxiv.org/abs/2406.04321 · HTML: https://mlsanity.com/paper/2406.04321/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 88 of its 107 references that are in the map.
- First posted on arXiv 6 Jun 2024.

## Builds on (most cited first, 30 of 88)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding](https://mlsanity.com/paper/2306.02858/) (2023)
- [Video-LLaVA: Learning United Visual Representation by Alignment Before Projection](https://mlsanity.com/paper/2311.10122/) (2023)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts](https://mlsanity.com/paper/2102.08981/) (2021)
- [ImageBind: One Embedding Space To Bind Them All](https://mlsanity.com/paper/2305.05665/) (2023)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips](https://mlsanity.com/paper/1906.03327/) (2019)
- [SoundStream: An End-to-End Neural Audio Codec](https://mlsanity.com/paper/2107.03312/) (2021)
- [Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers](https://mlsanity.com/paper/2301.02111/) (2023)
- [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://mlsanity.com/paper/2211.06687/) (2022)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [High Fidelity Neural Audio Compression](https://mlsanity.com/paper/2210.13438/) (2022)
- [Towards Automatic Learning of Procedures from Web Instructional Videos](https://mlsanity.com/paper/1703.09788/) (2017)

## Built on by (most cited first, 30 of 40)

- [Foundation Models for Music: A Survey](https://mlsanity.com/paper/2408.14340/) (2024)
- [Taming Data and Transformers for Audio Generation](https://mlsanity.com/paper/2406.19388/) (2024)
- [AudioX: A Unified Framework for Anything-to-Audio Generation](https://mlsanity.com/paper/2503.10522/) (2025)
- [Generative AI for Cel-Animation: A Survey](https://mlsanity.com/paper/2501.06250/) (2025)
- [Generative AI for Film Creation: A Survey of Recent Advances](https://mlsanity.com/paper/2504.08296/) (2025)
- [OmniAudio: Generating Spatial Audio from 360-Degree Video](https://mlsanity.com/paper/2504.14906/) (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)
- [Zero-Shot Audio-Visual Editing via Cross-Modal Delta Denoising](https://mlsanity.com/paper/2503.20782/) (2025)
- [MMTrail: A Multimodal Trailer Video Dataset with Language and Music Descriptions](https://mlsanity.com/paper/2407.20962/) (2024)
- [AudioGen-Omni: A Unified Multimodal Diffusion Transformer for Video-Synchronized Audio, Speech, and Song Generation](https://mlsanity.com/paper/2508.00733/) (2025)
- [Audio-Omni: Extending Multi-modal Understanding to Versatile Audio Generation and Editing](https://mlsanity.com/paper/2604.10708/) (2026)
- [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)
- [Unison: Harmonizing Motion, Speech, and Sound for Human-Centric Audio-Video Generation](https://mlsanity.com/paper/2605.08729/) (2026)
- [FilmComposer: LLM-Driven Music Production for Silent Film Clips](https://mlsanity.com/paper/2503.08147/) (2025)
- [DeepAudio-V1:Towards Multi-Modal Multi-Stage End-to-End Video to Speech and Audio Generation](https://mlsanity.com/paper/2503.22265/) (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)
- [Extending Visual Dynamics for Video-to-Music Generation](https://mlsanity.com/paper/2504.07594/) (2025)
- [Automatic Stage Lighting Control: Is it a Rule-Driven Process or Generative Task?](https://mlsanity.com/paper/2506.01482/) (2025)
- [UniMoE-Audio: Unified Speech and Music Generation with Dynamic-Capacity MoE](https://mlsanity.com/paper/2510.13344/) (2025)
- [Bob's Confetti: Phonetic Memorization Attacks in Music and Video Generation](https://mlsanity.com/paper/2507.17937/) (2025)
- [Controllable Video-to-Music Generation with Multiple Time-Varying Conditions](https://mlsanity.com/paper/2507.20627/) (2025)
- [V2M-Zero: Zero-Pair Time-Aligned Video-to-Music Generation](https://mlsanity.com/paper/2603.11042/) (2026)
- [Generative AI for Video Trailer Synthesis: From Extractive Heuristics to Autoregressive Creativity](https://mlsanity.com/paper/2604.04953/) (2026)
- [Video-Robin: Autoregressive Diffusion Planning for Intent-Grounded Video-to-Music Generation](https://mlsanity.com/paper/2604.17656/) (2026)
- [Cross-Modal Learning for Music-to-Music-Video Description Generation](https://mlsanity.com/paper/2503.11190/) (2025)
- [VidAudio-Bench: Benchmarking V2A and VT2A Generation across Four Audio Categories](https://mlsanity.com/paper/2604.10542/) (2026)
- [BEAT: Rhythm-Elastic Alignment for Agentic Music-guided Movie Trailer Generation](https://mlsanity.com/paper/2605.27067/) (2026)

## Abstract (arXiv)

In this work, we systematically study music generation conditioned solely on the video. First, we present a large-scale dataset comprising 360K video-music pairs, including various genres such as movie trailers, advertisements, and documentaries. Furthermore, we propose VidMuse, a simple framework for generating music aligned with video inputs. VidMuse stands out by producing high-fidelity music that is both acoustically and semantically aligned with the video. By incorporating local and global visual cues, VidMuse enables the creation of musically coherent audio tracks that consistently match the video content through Long-Short-Term modeling. Through extensive experiments, VidMuse outperforms existing models in terms of audio quality, diversity, and audio-visual alignment. The code and datasets are available at https://vidmuse.github.io/.