Video-Robin: Autoregressive Diffusion Planning for Intent-Grounded Video-to-Music Generation
Video-Robin combines autoregressive planning and diffusion synthesis for video-to-music generation, achieving 2.21x faster inference.
Key Findings
Methodology
Video-Robin integrates autoregressive planning with diffusion synthesis. The autoregressive module models global structure by aligning visual and textual inputs to produce high-level music latents, which are refined into coherent, high-fidelity music using local Diffusion Transformers. This approach enables fine-grained creator control without sacrificing audio realism.
Key Results
- Video-Robin achieves 2.21x faster inference than the fastest baseline, with an average FAD of 2.69, significantly outperforming Video2Music's 8.55s and VidMuse's 41.55s.
- On ReelBench, LORIS, and V2MBench, Video-Robin excels in audio quality and diversity.
- In both in-distribution and out-of-distribution benchmarks, Video-Robin outperforms existing methods in generated audio quality and audio-visual alignment.
Significance
Video-Robin provides a novel approach in the video-to-music generation field by integrating text and visual inputs, achieving higher semantic alignment and music generation quality. This method not only enhances the quality and speed of generated music but also offers creators greater control, addressing the limitations of existing tools in dynamic responsiveness and stylistic control.
Technical Contribution
Video-Robin offers a new method for generating music by combining autoregressive planning with diffusion synthesis. This method achieves fine-grained creator control without sacrificing audio realism and significantly improves inference speed.
Novelty
Video-Robin is the first to introduce text conditioning into video-to-music generation, combining autoregressive and diffusion models to provide higher semantic alignment and music generation quality.
Limitations
- The model may struggle with semantic alignment in extremely complex visual scenes.
- Generated music may lack diversity in certain musical styles.
Future Work
Future work could explore improving the model's performance in more diverse musical styles and more complex visual scenes, as well as further optimizing inference speed.
AI Executive Summary
Video-Robin is a novel video-to-music generation model that combines autoregressive planning with diffusion synthesis to achieve fast, high-quality music generation. Existing video-to-music generation tools typically rely on visual conditioning, limiting users' semantic and stylistic control. Video-Robin introduces text conditioning, combined with visual input, to achieve higher semantic alignment and music generation quality.
The core of the model lies in the combination of an autoregressive module and local Diffusion Transformers. The autoregressive module generates high-level music latents through visual and textual inputs, achieving semantic alignment. Subsequently, local Diffusion Transformers refine these latents into high-fidelity music. Experimental results show that Video-Robin achieves 2.21x faster inference than the fastest baseline, excelling in audio quality and diversity.
Video-Robin provides creators with greater control, enabling the generation of background music that matches both the visual content and the creator's intent. The innovation of this method lies in its integration of text and visual inputs, achieving higher semantic alignment and music generation quality, bringing new possibilities to the video-to-music generation field.
Deep Analysis
Background
Video-to-music generation is a multimodal task aimed at generating background music for video content. Existing methods often rely on visual conditioning, limiting users' semantic and stylistic control. Recently, autoregressive and diffusion models have shown promise in generation tasks, offering new possibilities.
Core Problem
Existing video-to-music generation tools fall short in dynamic responsiveness and stylistic control, struggling to generate music that aligns closely with visual content and creator intent. Achieving fine-grained creator control without sacrificing audio realism is a critical challenge.
Innovation
Video-Robin combines autoregressive planning with diffusion synthesis to achieve semantic alignment of text and visual inputs. The autoregressive module generates high-level music latents, which are refined into high-fidelity music by local Diffusion Transformers. This approach offers higher semantic alignment and music generation quality.
Methodology
- �� Autoregressive Module: Generates high-level music latents through visual and textual inputs, achieving semantic alignment.
- �� Local Diffusion Transformers: Refine the latents generated by the autoregressive module into high-fidelity music.
- �� Training Process: Employs a multi-stage training strategy, first pretraining on text-to-music, then fine-tuning on video-to-music.
Experiments
Experiments were conducted on ReelBench, LORIS, and V2MBench, evaluating the model's performance in audio quality, diversity, and audio-visual alignment. Results show that Video-Robin outperforms existing methods in both inference speed and generation quality.
Results
Video-Robin achieves 2.21x faster inference than the fastest baseline, with an average FAD of 2.69, significantly outperforming Video2Music's 8.55s and VidMuse's 41.55s.
Applications
Video-Robin can be used on short video platforms, providing creators with a tool for generating higher quality and more controllable background music, enhancing the appeal and dissemination of video content.
Limitations & Outlook
The model may struggle with semantic alignment in extremely complex visual scenes. Additionally, generated music may lack diversity in certain musical styles. Future work could explore improving the model's performance in more diverse musical styles and more complex visual scenes.
Plain Language Accessible to non-experts
Imagine you're creating a short video and need to add suitable background music. Video-Robin acts as your music creation assistant, generating music that matches the video's content and your textual description. It's like a chef in a kitchen, where you describe the dish you want, and the chef uses your description and ingredients to quickly cook up a delicious meal. Video-Robin combines video and text inputs, much like a chef combines ingredients and a recipe, to provide music that meets your needs.
ELI14 Explained like you're 14
Imagine you're playing a game and need to choose background music for your character. Video-Robin is like your music assistant, generating suitable music based on the game scene and your description. It's like choosing the right filter on social media to match your photo. Video-Robin combines video and text inputs to provide music that fits your needs. Isn't that cool?
Glossary
Autoregressive
A generative model that predicts each element in a sequence step-by-step to generate a complete sequence.
Used to generate high-level music latents for semantic alignment.
Diffusion Model
A generative model that produces high-quality samples through a step-by-step denoising process.
Used to refine latents generated by the autoregressive module into high-fidelity music.
FAD (Fréchet Audio Distance)
A metric for evaluating the quality of generated audio, with lower values indicating higher quality.
Used to assess the quality of the model's generated audio.
Multimodal
Involving multiple input forms, such as text, images, and audio.
Video-Robin combines text and visual inputs for semantic alignment.
Latent Variable
In generative models, an unobserved variable representing the underlying structure of the data.
Used to represent high-level music structure for diffusion model refinement.
Open Questions Unanswered questions from this research
- 1 How can the model improve semantic alignment in more complex visual scenes?
- 2 How can the diversity of generated music be enhanced across more musical styles?
Applications
Immediate Applications
Short Video Platforms
Provides short video creators with a tool for generating high-quality background music, enhancing the appeal and dissemination of video content.
Long-term Vision
Music Creation Assistance
Provides professional music creation tools, combining multimodal inputs for a more efficient music creation process.
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.