Video-Robin: Autoregressive Diffusion Planning for Intent-Grounded Video-to-Music Generation

Video-Robin: Autoregressive Diffusion Planning for Intent-Grounded Video-to-Music Generation

Introduction and Motivation

The task of video-to-music (V2M) generation is central in multimedia content creation, aiming to synthesize background music for a given video with coherency to the visual scene and, ideally, fine-grained control over stylistic and semantic intent. Existing models predominantly condition on visual signals and often provide limited avenues for creator-driven specification of musical style, structure, or affect. Video-Robin addresses this limitation by proposing a hybrid framework that integrates multi-scale hierarchical autoregressive planning with local diffusion-based refinement, incorporating both textual and visual conditioning for controllable, high-fidelity music generation. The framework explicitly grounds music generation in creator intent via text prompts, formalizing and empirically advancing the text+video-to-music generation task.

Figure 1: Video-Robin receives video frames and a text prompt, generating semantically-aligned music that responds dynamically to the visual scene (e.g., crackling fire, rustling wind).

Architecture

Video-Robin employs a modular architecture that decomposes music generation into (i) semantic-level autoregressive planning and (ii) patchwise local refinement. The model leverages CLIP-based framewise visual encodings and textual embeddings, jointly processed in the AR-Head, which consists of a Multimodal Semantic LLM, a Finite Scalar Quantization (FSQ) bottleneck, and a Residual Integration Transformer Encoder (RITE). This head produces semantically-structured latent patches, encapsulating global musical structure and intent.

The Loc-DiT Refinement-Head, conditioned on the AR plan and previous latent history, performs local denoising through a Diffusion Transformer, ensuring fine-grained, high-fidelity acoustic detail. The resulting sequence of latent patches is finally decoded via a frozen VAE, inherited from SongBloom. Training occurs in two stages: pretraining on large-scale text-to-music and subsequent finetuning with video context projection. This design unifies expression of both creative intent and audiovisual dynamics, while overcoming the trade-off between structural coherency of AR models and the fidelity of diffusion processes.

Figure 3: Robin Architecture: video+text are decoded via autoregressive planning and Diffusion Transformer refinement into VAE latent patches, synthesizing temporally aligned, controlled music.

Data Pipeline and Benchmarking

Large-scale, intent-aligned benchmarks are lacking in V2M research. Video-Robin introduces ReelBench, comprising 300 curated video-music pairs with text prompts augmented for fine-grained musical control (e.g., key, tempo, chord progression, emotional category). Prompts are constructed using automated pipelines with MusicFlamingo to extract musical attributes and Qwen3-8B for text paraphrasing and contextual fusion, facilitating high-control training and comprehensive evaluation.

Figure 4: Data preprocessing pipeline integrating MusicFlamingo for musical attribute extraction and Qwen3-8B for semantic prompt fusion.

Experimental Results

Video-Robin is evaluated across multiple benchmarks—ReelBench (in-domain), V2MBench, and LORIS (out-of-distribution)—using a diverse suite of metrics: FAD, FD, KL-divergence, IS, ImageBind alignment, Density, and Coverage. Comprehensive ablations establish the critical role of both the FSQ quantization bottleneck and RITE; the removal of either degrades performance, especially on perceptual and diversity axes, confirming the necessity of semi-discrete semantic planning supplemented by residual refinement. Patch size analysis reveals a clear trade-off: smaller patches enhance perceptual fidelity (lower FAD and FD), while larger patches favor distributional diversity (lower KL-divergence).

Quantitatively, the model achieves the lowest FAD and FD on ReelBench and LORIS, leading in Inception Score and coverage, and exhibiting competitive or superior performance to VidMuse and GVMGen baselines on V2MBench. Notably, Video-Robin is $2.21\times$ faster at inference than the fastest alternative (Video2Music), without compromising output quality.

Figure 2: (Left) Video-Robin achieves the fastest inference among strong baselines. (Right) It maintains the optimal trade-off between audio fidelity and speed across three evaluation sets.

Qualitative and Human Evaluation

Spectrogram analysis on diverse samples highlights superior spectral coverage, temporal stability, and adherence to prompt-specified musical attributes relative to visual-only models. Video-Robin generates music that more accurately tracks both fine-grained descriptors (e.g., "crystalline bells," "muted, low-pass-filtered") and dynamically responds to changes in visual content.

Figure 6: Spectrogram comparison (meditative ambient drone): Video-Robin best captures high-frequency detail linked to prompt descriptors.

Figure 8: Spectrogram comparison (mid-tempo electronic): Video-Robin yields structurally coherent low-frequency banding and matches prompt-instructed timbral features.

Human A/B testing (18 participants) reports highest preference across axes for Video-Robin: audio quality, musicality, audiovisual alignment, and overall experience. On ReelBench, Video-Robin is rated best in emotion alignment, instrumentation fit, and temporal correspondence when compared with leading text-conditioned and video-only models.

Figure 9: Human A/B test results showing Video-Robin preferred for audio quality, musicality, video-music alignment, and overall integration.

Role and Importance of Textual Conditioning

Ablation reveals that omitting text prompts results in consistently degraded quality and diversity, though alignment does not collapse entirely; explicit intent grounding via text substantially improves both objective audio metrics and human-rated relevance.

Theoretical and Practical Implications

Video-Robin analytically demonstrates the efficacy of two-stage, autoregressive-diffusion frameworks in multimodal, intent-grounded music generation. The introduction of planning-refinement separation, local quantization with residual integration, and multi-stage alignment transfer from text-to-music to video-to-music represents a principled advancement over previous monolithic or vision-only approaches. This modular, hierarchical approach can generalize to longer-form music, adaptive generation with live video editing, and other creative workflows where semantic and stylistic control is paramount.

On the practical front, Video-Robin's significant acceleration in inference time, rigorous alignment to intent and scene, and user-preferred performance make it suited for integration in automated content creation pipelines for social video, vlogging, and commercial editing platforms. The open-source release of ReelBench benchmarks may catalyze further study in multimodal grounding and controllable audio generation systems.

Limitations and Future Directions

The current system is circumscribed to short (10s) video clips, instrumental music (no vocals), and VAE-limited latent coverage. Long-form narrative music, seamless scoring across scene changes, and integration of learned latent spaces remain open research avenues. Further, the subjective and task-specific challenge of measuring intent-following in music necessitates new evaluation protocols, such as structural, harmonic, or pacing alignment metrics, as well as larger and more diverse intent-annotated datasets. Integrating more adaptive (possibly non-frozen) encoders, richer user interaction (inpainting, editing), and explorable latent spaces can extend applicability and creative relevance.

Conclusion

Video-Robin establishes a new standard for controllable, creator-intent-grounded video-to-music generation by harmonizing semantic autoregressive planning with local diffusion refinement, robustly supporting multimodal conditioning and outperforming extant models across fidelity, diversity, alignment, and speed metrics. Its architectural and benchmarking contributions concretely advance the state of generative multimodal audio models, with significant theoretical and practical implications for the next generation of synthesis tools.

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