# VIBE: Video Instruction-aligned Background music gEneration

arXiv: https://arxiv.org/abs/2608.30125 · HTML: https://mlsanity.com/paper/2608.30125/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Preference alignment for recommenders and RAG, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 27 of its 39 references that are in the map.
- First posted on arXiv 31 Aug 2026.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Preference alignment for recommenders and RAG](https://mlsanity.com/alignment/preference-alignment-for-recommenders-and-rag/)

## Builds on (most cited first, 27 of 27)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [ImageBind: One Embedding Space To Bind Them All](https://mlsanity.com/paper/2305.05665/) (2023)
- [Reliable Fidelity and Diversity Metrics for Generative Models](https://mlsanity.com/paper/2002.09797/) (2020)
- [DiffusionNFT: Online Diffusion Reinforcement with Forward Process](https://mlsanity.com/paper/2509.16117/) (2025)
- [Mustango: Toward Controllable Text-to-Music Generation](https://mlsanity.com/paper/2311.08355/) (2023)
- [DiTAR: Diffusion Transformer Autoregressive Modeling for Speech Generation](https://mlsanity.com/paper/2502.03930/) (2025)
- [Video Background Music Generation with Controllable Music Transformer](https://mlsanity.com/paper/2111.08380/) (2021)
- [ACE-Step: A Step Towards Music Generation Foundation Model](https://mlsanity.com/paper/2506.00045/) (2025)
- [MiniCPM4: Ultra-Efficient LLMs on End Devices](https://mlsanity.com/paper/2506.07900/) (2025)
- [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)
- [On the Properties of Kullback-Leibler Divergence Between Multivariate Gaussian Distributions](https://mlsanity.com/paper/2102.05485/) (2021)
- [SongBloom: Coherent Song Generation via Interleaved Autoregressive Sketching and Diffusion Refinement](https://mlsanity.com/paper/2506.07634/) (2025)
- [JamendoMaxCaps: A Large Scale Music-caption Dataset with Imputed Metadata](https://mlsanity.com/paper/2502.07461/) (2025)
- [HeartMuLa: A Family of Open Sourced Music Foundation Models](https://mlsanity.com/paper/2601.10547/) (2026)
- [HarmonySet: A Comprehensive Dataset for Understanding Video-Music Semantic Alignment and Temporal Synchronization](https://mlsanity.com/paper/2503.01725/) (2025)
- [Multimodal Music Generation with Explicit Bridges and Retrieval Augmentation](https://mlsanity.com/paper/2412.09428/) (2024)
- [MR-FlowDPO: Multi-Reward Direct Preference Optimization for Flow-Matching Text-to-Music Generation](https://mlsanity.com/paper/2512.10264/) (2025)
- [CMI-RewardBench: Evaluating Music Reward Models with Compositional Multimodal Instruction](https://mlsanity.com/paper/2603.00610/) (2026)
- [Semantic Routing: Exploring Multi-Layer LLM Feature Weighting for Diffusion Transformers](https://mlsanity.com/paper/2602.03510/) (2026)
- [VidTune: Creating Video Soundtracks with Generative Music and Contextual Thumbnails](https://mlsanity.com/paper/2601.12180/) (2026)
- [Omni-Judge: Can Omni-LLMs Serve as Human-Aligned Judges for Text-Conditioned Audio-Video Generation?](https://mlsanity.com/paper/2602.01623/) (2026)
- [V2M-Zero: Zero-Pair Time-Aligned Video-to-Music Generation](https://mlsanity.com/paper/2603.11042/) (2026)
- [Video-Robin: Autoregressive Diffusion Planning for Intent-Grounded Video-to-Music Generation](https://mlsanity.com/paper/2604.17656/) (2026)
- [SymphonyGen: 3D Hierarchical Orchestral Generation with Controllable Harmony Skeleton](https://mlsanity.com/paper/2604.25498/) (2026)
- [Dialogue-Aware Video-to-Music Generation Using Public Domain Film Collections](https://mlsanity.com/paper/2608.11576/) (2026)

## Abstract (arXiv)

Current video-to-music (V2M) models lack semantic control and fail to penalize instruction violations, largely due to their reliance on reconstruction objectives and the representational bottleneck of static cross-modal conditioning in Diffusion Autoregressive (DAR) architectures. To resolve this, we introduce VIBE, a novel text-and-video-to-music (T+V2M) generation model that leverages: (1) Conditioning Connection, a depth-wise cross-layer conditioning mechanism that dynamically bridges the planning and diffusion refinement heads and (2) a comprehensive reward modeling taxonomy, optimizing for both hard, verifiable constraints (e.g., tempo, key) and soft, subjective qualities (e.g., musicality, multimodal alignment) with a structured 5-stage training curriculum. Upon evaluation using audio-visual alignment, instruction following, and audio quality metrics, along with a subjective human evaluation study, we observe that VIBE demonstrates enhanced controllability and instruction adherence while performing comparably to most evaluated baselines on generation fidelity and multimodal alignment.