PrismAudio: Decomposed Chain-of-Thoughts and Multi-dimensional Rewards for Video-to-Audio Generation
Abstract
Video-to-Audio (V2A) generation requires balancing four critical perceptual dimensions: semantic consistency, audio-visual temporal synchrony, aesthetic quality, and spatial accuracy; yet existing methods suffer from objective entanglement that conflates competing goals in single loss functions and lack human preference alignment. We introduce PrismAudio, the first framework to integrate Reinforcement Learning into V2A generation with specialized Chain-of-Thought (CoT) planning. Our approach decomposes monolithic reasoning into four specialized CoT modules (Semantic, Temporal, Aesthetic, and Spatial CoT), each paired with targeted reward functions. This CoT-reward correspondence enables multidimensional RL optimization that guides the model to jointly generate better reasoning across all perspectives, solving the objective entanglement problem while preserving interpretability. To make this optimization computationally practical, we propose Fast-GRPO, which employs hybrid ODE-SDE sampling that dramatically reduces the training overhead compared to existing GRPO implementations. We also introduce AudioCanvas, a rigorous benchmark that is more distributionally balanced and covers more realistically diverse and challenging scenarios than existing datasets, with 300 single-event classes and 501 multi-event samples. Experimental results demonstrate that PrismAudio achieves state-of-the-art performance across all four perceptual dimensions on both the in-domain VGGSound test set and out-of-domain AudioCanvas benchmark. The project page is available at https://PrismAudio-Project.github.io.
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
PrismAudio presents a novel framework for Video-to-Audio generation that effectively addresses the challenges of objective entanglement through the use of specialized Chain-of-Thought modules and Reinforcement Learning. The contributions made in methodology, dataset creation, and experimental validation mark a significant step forward in the field of audio generation, although the paper would benefit from improved reproducibility and a more thorough exploration of its limitations.
Methodology
The methodology presented in PrismAudio is innovative, particularly in its integration of Reinforcement Learning (RL) with specialized Chain-of-Thought (CoT) modules. The decomposition of the V2A generation task into four distinct CoT modules (Semantic, Temporal, Aesthetic, and Spatial) is a significant advancement that addresses the issue of objective entanglement in existing models. The targeted reward functions associated with each module allow for a more nuanced optimization process, which is a notable improvement over traditional single loss function approaches. Furthermore, the introduction of Fast-GRPO for efficient training is commendable, as it enhances the practicality of the proposed framework.
Results & evaluation
The experimental results are robust, showcasing state-of-the-art performance across multiple perceptual dimensions on both the in-domain VGGSound test set and the out-of-domain AudioCanvas benchmark. The creation of the AudioCanvas dataset itself is a valuable contribution, as it provides a more balanced and diverse set of scenarios for evaluating V2A generation models. However, the paper could benefit from a more detailed analysis of the experimental setup and the specific metrics used to assess performance.
Reproducibility
The paper lacks sufficient detail regarding the implementation, which could hinder reproducibility. While it mentions the use of hybrid ODE-SDE sampling, further specifics on the architecture, hyperparameters, and training procedures would be beneficial for other researchers looking to replicate the results. The absence of a publicly available code repository is a significant drawback in this regard.
Limitations
One limitation is the potential complexity introduced by the multi-dimensional optimization process, which may require careful tuning of the reward functions to achieve optimal performance. Additionally, while the paper claims state-of-the-art results, it does not sufficiently address how the model performs in edge cases or under varying conditions that may not be represented in the training data.
Broader impact
The implications of this research are substantial, as it could pave the way for more sophisticated audio generation systems that can be applied in various fields, including film, gaming, and virtual reality. By improving the alignment of generated audio with visual content, this work has the potential to enhance user experience in multimedia applications. PrismAudio presents a novel framework for Video-to-Audio generation that effectively addresses the challenges of objective entanglement through the use of specialized Chain-of-Thought modules and Reinforcement Learning. The contributions made in methodology, dataset creation, and experimental validation mark a significant step forward in the field of audio generation, although the paper would benefit from improved reproducibility and a more thorough exploration of its limitations.
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