Video-Robin: Autoregressive Diffusion Planning for Intent-Grounded Video-to-Music Generation
Abstract
Video-to-music (V2M) is the fundamental task of creating background music for an input video. Recent V2M models achieve audiovisual alignment by typically relying on visual conditioning alone and provide limited semantic and stylistic controllability to the end user. In this paper, we present Video-Robin, a novel text-conditioned video-to-music generation model that enables fast, high-quality, semantically aligned music generation for video content. To balance musical fidelity and semantic understanding, Video-Robin integrates autoregressive planning with diffusion-based synthesis. Specifically, an autoregressive module models global structure by semantically aligning visual and textual inputs to produce high-level music latents. These latents are subsequently refined into coherent, high-fidelity music using local Diffusion Transformers. By factoring semantically driven planning into diffusion-based synthesis, Video-Robin enables fine-grained creator control without sacrificing audio realism. Our proposed model outperforms baselines that solely accept video input and additional feature conditioned baselines on both in-distribution and out-of-distribution benchmarks with a 2.21x speed in inference compared to SOTA. We will open-source everything upon paper acceptance.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
Video-Robin presents a novel approach to video-to-music generation by integrating autoregressive planning with diffusion-based synthesis, significantly enhancing the ability to create semantically aligned music for video content. The comprehensive experimental evaluation and introduction of a new benchmark highlight its technical contributions and relevance to the field of machine learning in audio generation.
Methodology
The methodology presented in Video-Robin is innovative, integrating autoregressive planning with diffusion-based synthesis to enhance video-to-music generation. The use of a multimodal semantic language model for planning and a refinement head utilizing local Diffusion Transformers is a notable advancement. The architecture effectively balances musical fidelity and semantic understanding, allowing for fine-grained control over the generated music. The two-stage training strategy, which includes pretraining on text-to-music generation followed by video-to-music fine-tuning, is a well-structured approach that leverages existing datasets effectively.
Results & evaluation
The experiments are comprehensive, utilizing both in-distribution and out-of-distribution benchmarks to validate the model's performance. The introduction of the ReelBench dataset is a significant contribution, providing a structured evaluation framework for text-conditioned video-to-music generation. The results demonstrate that Video-Robin outperforms existing models on multiple metrics, showcasing its effectiveness in generating high-quality, semantically aligned music. The use of human evaluations alongside quantitative metrics adds depth to the experimental assessment.
Reproducibility
The paper provides detailed implementation specifics, including architecture configurations, training procedures, and evaluation metrics, which enhance reproducibility. However, the lack of open-source code or demo links limits the ability for others to replicate the results directly. The authors mention plans to open-source their work upon acceptance, which is a positive step towards improving reproducibility.
Limitations
The paper acknowledges limitations, such as the focus on short-form videos and the dependency on frozen representation components, which may restrict the model's expressivity in niche genres. The evaluation metrics, while comprehensive, may not fully capture the nuances of music-video alignment and creator intent, suggesting a need for more refined metrics in future work.
Broader impact
The potential applications of Video-Robin are significant, particularly in the context of content creation for social media platforms. By enabling creators to generate music that aligns with their artistic intent, the model could enhance the creative process and democratize access to high-quality music production. The integration of multimodal inputs also opens avenues for further research in generative models that combine audio and visual data. Video-Robin presents a novel approach to video-to-music generation by integrating autoregressive planning with diffusion-based synthesis, significantly enhancing the ability to create semantically aligned music for video content. The comprehensive experimental evaluation and introduction of a new benchmark highlight its technical contributions and relevance to the field of machine learning in audio generation.
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