PAVAS: Physics-Aware Video-to-Audio Synthesis
Abstract
Recent advances in Video-to-Audio (V2A) generation have achieved impressive perceptual quality and temporal synchronization, yet most models remain appearance-driven, capturing visual-acoustic correlations without considering the physical factors that shape real-world sounds. We present Physics-Aware Video-to-Audio Synthesis (PAVAS), a method that incorporates physical reasoning into a latent diffusion-based V2A generation through the Physics-Driven Audio Adapter (Phy-Adapter). The adapter receives object-level physical parameters estimated by the Physical Parameter Estimator (PPE), which uses a Vision-Language Model (VLM) to infer the moving-object mass and a segmentation-based dynamic 3D reconstruction module to recover its motion trajectory for velocity computation. These physical cues enable the model to synthesize sounds that reflect underlying physical factors. To assess physical realism, we curate VGG-Impact, a benchmark focusing on object-object interactions, and introduce Audio-Physics Correlation Coefficient (APCC), an evaluation metric that measures consistency between physical and auditory attributes. Comprehensive experiments show that PAVAS produces physically plausible and perceptually coherent audio, outperforming existing V2A models in both quantitative and qualitative evaluations. Visit https://physics-aware-video-to-audio-synthesis.github.io for demo videos.
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
The main contribution of this paper is the introduction of PAVAS, a physics-aware framework for Video-to-Audio synthesis that significantly enhances the realism of generated audio by incorporating physical parameters. This innovative approach not only advances the state-of-the-art in V2A generation but also opens new avenues for research in the intersection of machine learning and physical modeling.
Methodology
The proposed PAVAS method introduces a novel approach to Video-to-Audio synthesis by integrating physical reasoning into the generation process. The use of a Physics-Driven Audio Adapter (Phy-Adapter) that leverages object-level physical parameters is a significant advancement over traditional appearance-driven methods. The incorporation of a Vision-Language Model (VLM) for inferring physical parameters is innovative and adds depth to the model's capability to generate realistic audio. However, the methodology could benefit from a more detailed explanation of the dynamic 3D reconstruction module and its integration with the Phy-Adapter.
Results & evaluation
The experiments are comprehensive, employing both quantitative and qualitative evaluations to demonstrate the effectiveness of PAVAS. The introduction of the VGG-Impact benchmark and the Audio-Physics Correlation Coefficient (APCC) as evaluation metrics are commendable, providing a structured way to assess physical realism in audio synthesis. The results indicate that PAVAS outperforms existing models, but further details on the datasets used and the specific metrics for comparison would strengthen the evaluation.
Reproducibility
The paper lacks detailed implementation specifics that would aid in reproducibility. While the demo URL provides a visual representation of the results, the absence of a public code repository limits the ability for other researchers to replicate the findings fully. Including a GitHub repository or similar would enhance the paper's impact and utility.
Limitations
One limitation is the reliance on the accuracy of the Physical Parameter Estimator (PPE) and the segmentation-based dynamic 3D reconstruction module, which may introduce errors if the input video quality is subpar. Additionally, the paper does not address the scalability of the approach to more complex scenes or diverse environments, which could affect generalization.
Broader impact
The integration of physical reasoning into audio synthesis has the potential to revolutionize applications in multimedia content creation, gaming, and virtual reality, where realistic soundscapes are crucial. This work could inspire further research into physics-aware models across various domains, enhancing the realism of synthetic media. The main contribution of this paper is the introduction of PAVAS, a physics-aware framework for Video-to-Audio synthesis that significantly enhances the realism of generated audio by incorporating physical parameters. This innovative approach not only advances the state-of-the-art in V2A generation but also opens new avenues for research in the intersection of machine learning and physical modeling.
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