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VIBE: Video Instruction-aligned Background music gEneration
Aryan Vijay Bhosale, Vaibhavi Lokegaonkar, Vishnu Raj, Gouthaman KV, Sreyan Ghosh, Ramani Duraiswami, Lie Lu, Dinesh Manocha
TL;DR
Existing video-to-music models offer limited semantic control and do not explicitly penalize violations of individual musical instructions. VIBE addresses this with dynamic cross-layer Conditioning Connection and holistic reward modeling, achieving stronger instruction adherence while remaining competitive on fidelity and multimodal alignment.
Problem
Existing video-to-music models provide limited semantic and stylistic controllability and lack explicit penalties for violations of individual musical instructions.
Method
VIBE combines depth-wise Conditioning Connection with holistic reward modeling and a structured 5-stage training curriculum for text-and-video-to-music generation.
Results
Human evaluation confirms preference for VIBE across all assessment axes, while experiments report consistent gains in fine-grained instruction adherence and competitive generation fidelity.
Takeaways & Limitations
VIBE demonstrates enhanced controllability and instruction adherence while remaining competitive on generation fidelity and multimodal alignment.
Takeaways & Limitations
VIBE currently targets 10-second instrumental clips, does not support vocal music, long-form scoring, or interactive editing, and relies on constrained reward and alignment proxies.
Abstract
from arXiv · showhide
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.
1 Introduction
VIBE targets limited semantic and stylistic control in video-to-music generation by combining dynamic multimodal conditioning with explicit preference optimization for fine-grained musical instructions. Experiments report stronger instruction adherence while remaining competitive in generation fidelity.
- Motivation: Existing video-to-music systems provide limited semantic and stylistic controllability, especially for fine-grained tempo, key, genre, and mood specification.Reconstruction-based training lacks an explicit mechanism for penalizing misalignment in individual instruction components.
- Approach: VIBE introduces joint text-and-video-to-music generation with a structured training curriculum for controllable alignment with textual and visual prompts.The approach combines dynamic conditioning with multi-preference optimization.
- Approach: Conditioning Connection passes learned combinations of multimodal language-model hidden states to each LocDiT layer, addressing static-conditioning bottlenecks.This supports multimodal grounding and propagation of context through the denoising hierarchy.
- Preference optimization: VIBE organizes musical attributes into hard, verifiable rewards and learned cross-modal or omni-modal rewards for subjective perceptual qualities.The taxonomy covers objective instructions and broader qualities such as multimodal alignment.
- Preference optimization: A multistage DiffusionNFT-style training recipe integrates compositional rewards for preference optimization under multimodal instruction-following constraints.The recipe uses specified data mixtures for Diffusion Autoregressive architectures.
- Results: Human evaluation and objective metrics demonstrate consistent gains in fine-grained instruction adherence over prior text- and video-conditioned systems while remaining competitive on generation fidelity.The reported evaluation spans Reelbench objective metrics and human assessment.
2 Related Work
Prior work largely centers on visual feature extraction and offers limited control or preference alignment for music generation. VIBE extends preference-aligned generation to video conditioning and fine-grained text instructions while addressing static conditioning.
- Video-to-music generation: Foundational video-to-music systems use motion and semantic video features to generate symbolic MIDI music.CMT and Video2Music are identified as examples.
- Video-to-music generation: Later systems generate high-fidelity waveforms or incorporate visual and textual encoders, but existing approaches remain limited in preference optimization and expressivity.The cited discussion describes open challenges in modeling human perceptual preferences and relative quality across broader candidate pools.
- Dynamic conditioning: Diffusion-based systems suffer from static conditioning because fixed inputs do not adapt to the differing requirements of Diffusion Transformer layers.VIBE addresses this limitation with Conditioning Connection.
- Preference optimization: Preference-aligned music-generation methods use multidimensional automated rewards, while VIBE extends preference optimization to video-conditioned generation with fine-grained text instructions.The related-work discussion positions VIBE as an extension of preference-aligned music generation.
3 Methodology
VIBE addresses fine-grained instruction following and multimodal alignment through depth-wise dynamic conditioning and compositional reward modeling. Its architecture links global planning with patch-wise diffusion refinement, while training combines supervised diffusion objectives with online preference optimization.
- Methodology: VIBE treats instruction following and multimodal alignment as separate challenges requiring dynamic context propagation and explicit penalties for attribute violations.
- Architecture: Conditioning Connection replaces static conditioning by combining hidden states from all multimodal language-model layers for each LocDiT layer.Learnable coefficients and a projection produce layer-specific conditioning vectors.
- Architecture: Each LocDiT layer uses current multimodal context and previously generated musical patches, promoting patch continuity and rhythmic and semantic coherence.
- Training Objectives: The training objectives include flow-matching diffusion loss during pretraining and supervised finetuning, followed by Diffusion-NFT online reinforcement learning over multiple generated candidates.Candidates are scored with an optimality probability and used to define positive and negative policies.
- Reward Modelling: Reward modeling separates hard verifiable attributes such as tempo and key from soft perceptual attributes such as genre, mood, and multimodal alignment.Hard rewards use measurable audio properties, while soft rewards use learned cross-modal or omni-modal judgments.
- Reward Modelling: Tempo rewards handle exact, range, and descriptive targets and evaluate estimated BPM at ˆb, 2ˆb, and ˆb/2 to reduce octave errors.
- Reward Modelling: Key rewards combine interpretable Circle-of-Fifths distance with a spectrum-based Krumhansl-Schmuckler reward that remains robust under detector failure.
Romni
The training curriculum progresses from text-music pre-training to instruction-following and preference optimization, using composite rewards that combine subjective qualities with verifiable tempo and key constraints. Figure 2 and Table 1 present the curriculum stages and their associated training datasets.
- Training curriculum: Stage 1 pre-trains the model on large-scale text-music pairs without video conditioning to align multimodal hidden states with musical latents.The AR-Head learns this mapping through the Refinement-Head, establishing the generative backbone for later stages.
- Training curriculum: Stage 2 uses MusicBench to improve instruction following while retaining Stage 1's architecture and training configuration.
- Training curriculum: Stage 3 applies DiffusionNFT-based online reinforcement learning with a composite text-to-music reward combining subjective-quality and hard tempo and key rewards.The rewards are computed on captions from the CMI-Pref Dataset, with multiple rollouts scored for negative-aware fine-tuning.
- Training curriculum: The curriculum's training datasets are summarized by stage in Table 1, with splits identified as training splits unless otherwise noted.
4 Experiments
VIBE is evaluated on instruction following, audio quality, multimodal alignment, ablations, and human preferences using ReelBench and complementary comparisons. It consistently improves fine-grained instruction adherence while remaining competitive in generation fidelity and alignment.
- Evaluation setup: Evaluation covers audio quality, audio-visual alignment, instruction following, baseline comparisons, ablations, and human A/B judgments.Audio quality uses FAD, FD, KL, IS, Density, and Coverage; alignment uses ImageBind and an omni-judge; instruction following uses tempo and key accuracy.
- Instruction alignment: VIBE outperforms Video-Robin and the Stage 5 ablation across all five tempo and key instruction-following metrics.Tempo accuracy rises under exact and octave-equivalent tolerances, Tempo MAE decreases, and VIBE achieves the highest exact and loose key accuracy.
- Quantitative comparison: VIBE achieves the best IS, FD Density, and Coverage among evaluated models, while reinforcement learning increases FAD and KL relative to Video-Robin.The reported trade-off exchanges distributional proximity for perceptual quality and instruction adherence, with IB also increasing relative to Video-Robin.
- Multimodal alignment: VIBE outperforms baselines on five of six ability axes and all evaluated baselines on overall omni-modal alignment.The authors supplement objective metrics with a seven-axis omni-judge evaluation and a subjective human evaluation because objective metrics have limitations.
- Ablations: Adding Conditioning Connection reduces FAD by 32.07% compared with the pretrained model.The base architecture has marginally higher IB, but its poor FAD indicates weaker high-fidelity music priors.
- Ablations: Combining all training stages with Conditioning Connection improves most audio-quality metrics, while the Stage 3 text-to-music model outperforms ACE-Step v1.5 with video captions as instructions.A separate comparison also finds the Qwen2.5-Omni-trained model outperforms the VMB baseline across all qualitative metrics.
5 Conclusion
VIBE addresses static cross-modal conditioning and missing instruction-following supervision through Conditioning Connection and a structured five-stage curriculum with holistic reward modeling. Experiments report strong perceptual quality, diversity, and instruction-following performance, with human evaluators preferring VIBE across assessment axes.
- 5 Conclusion: VIBE addresses static cross-modal conditioning and absent instruction-following supervision with Conditioning Connection and a structured 5-stage training curriculum.The approach also incorporates holistic reward modeling.
- 5 Conclusion: Experiments demonstrate strong performance on perceptual quality, generative diversity, and instruction-following metrics.
- 5 Conclusion: Human evaluation confirms preference for VIBE across all assessment axes.
Limitations
VIBE's scope and effectiveness are bounded by frozen base components, proxy and learned reward limitations, a short instrumental-clip setting, and added training overhead. The paper also identifies risks involving misuse, copyright, economic displacement, cultural bias, and deceptive media.
- Limitations: Frozen VAE and encoder constraints may limit VIBE's expressivity in niche genres.
- Limitations: ImageBind is not trained on music data and may not fully capture semantic correspondence between generated music and video.
- Limitations: VIBE currently targets 10-second instrumental clips and does not support vocal music, long-form scoring, or interactive editing.
- Limitations: Genre and mood rewards rely entirely on CMI-RM, while Qwen2.5-Omni reward extraction adds non-trivial inference overhead during Stage 5 training.
- Potential Risks: Generated scores could amplify harmful, misleading, or manipulative audiovisual content, while generated audio may resemble copyrighted works.
- Potential Risks: Scalable background-music generation may reduce demand for human composers and music licensors and may encode cultural or stylistic bias.
- Potential Risks: Combined with video generation and voice synthesis, automated scoring could lower the barrier to producing convincing synthetic audiovisual media.
D Instruction-Following Evaluation Protocol
The instruction-following protocol evaluates tempo and key using exact and permissive criteria. Tempo accepts octave-equivalent matches, while key accepts relative and parallel key matches under the loose criterion.
- D Instruction-Following Evaluation Protocol: Tempo is evaluated with exact and octave-equivalent tolerances alongside mean absolute error in BPM.
- D Instruction-Following Evaluation Protocol: Key is evaluated with exact and loose tolerances, with the loose criterion additionally accepting relative and parallel key matches.
Tempo Evaluation
Tempo evaluation estimates each generated clip's BPM and measures correctness under exact and octave-equivalent tolerances, together with mean absolute BPM error.
- Tempo Evaluation: Tempo is estimated in beats per minute using librosa's beat tracker.
- Tempo Evaluation: Exact Tempo Accuracy counts predictions within 10% relative error of the ground-truth tempo as correct.
- Tempo Evaluation: Octave-Equivalent Tempo Accuracy also accepts predictions at half or double the ground-truth tempo when they fall within the 10% tolerance.
- Tempo Evaluation: Tempo MAE reports mean absolute error in BPM across samples with valid ground-truth tempo annotations.
Key Evaluation
Key evaluation estimates each generated clip’s key from chroma profiles and compares it with annotated ground-truth keys using exact and permissive criteria.
- Key Estimation: Generated keys are estimated from constant-Q chromagrams averaged into 12-dimensional pitch-class profiles and correlated with major/minor templates.Enharmonic equivalents are treated as identical when parsing ground-truth annotations.
- Key Metrics: Exact Key Accuracy requires both predicted pitch class and mode to match the ground truth.
- Key Metrics: Loose Key Accuracy accepts exact, relative-key, or parallel-key matches under the specified pitch-class and mode conditions.
- Evaluation Scope: Both key metrics are computed only for ReelBench samples with valid ground-truth key annotations.
E Further Ablations and associated details
The ablations and supporting analyses examine reward composition, visual conditioning, alignment evaluation, human review, and instruction-following failures. Results indicate benefits from combining hard and soft rewards while documenting evaluation procedures and failure cases.
- Reward Design Ablation: Using both hard verifiable and soft rewards produces better audio quality than using either reward category alone.The reward-design ablation evaluates soft and hard components for generation quality and alignment performance.
- Visual Bridging: Table 10 evaluates visual bridging by comparing VIBE with the Visual Music Bridge Technique on generation quality and video–music alignment metrics.
- Reward Design Ablation: The hard reward combines soft reward terms with tempo and key rewards for penalizing unfulfilled musical attributes.
- Training Details: Offline Gemini captions provide four diverse music prompts per training video, with one sampled caption scored by CMI-RM at each RL step.This supplies a text-grounded video–music alignment proxy without invoking the video encoder during training.
- Human Evaluation: Human review used samples shown in Figure 4 and unpaid institutional volunteers aged 18–30.
- Instruction Following: Table 11 presents HarmonySet failure modes for fine-grained tempo and key adherence, including misses under octave-equivalence criteria.Successful examples show tempo errors of 0.3% and 5.8% within the stated tolerance band.
- Visual Conditioning Ablation: Table 12 compares text-to-music models using video captions as input to test whether text context can substitute for visual conditioning.The caption states that VIBE’s text-to-music model outperforms ACE-Step1.5.
- Alignment Evaluation: Gemini’s alignment evaluation uses seven independently rated axes, with Overall Alignment treated as a separate holistic judgment.