V2A-DPO: Omni-Preference Optimization for Video-to-Audio Generation
V2A-DPO enhances video-to-audio generation through Direct Preference Optimization, significantly improving semantic consistency and temporal alignment.
Key Findings
Methodology
The V2A-DPO framework integrates the AudioScore system, an automated preference pair generation pipeline, and a curriculum learning optimization strategy. AudioScore evaluates semantic consistency, temporal alignment, and perceptual quality of generated audio. By combining a small set of human-annotated preference pairs with automatically generated ones, a large dataset is formed for optimization. The curriculum learning strategy optimizes the model in stages, enhancing the quality and consistency of generated audio.
Key Results
- On the VGGSound dataset, V2A-DPO optimized MMAudio outperforms DDPO-optimized and pre-trained models in audio quality and semantic alignment, with IS increasing by 1.81, IB-score by 0.86, and DeSync decreasing by 0.09.
- The Frieren model also shows significant improvement under V2A-DPO optimization, especially in semantic consistency and temporal alignment.
- Ablation studies indicate that the curriculum learning strategy and KL divergence constraint parameter significantly impact model performance.
Significance
The V2A-DPO framework is significant in the video-to-audio generation field, addressing existing models' limitations in style control, aesthetic quality assessment, and comprehensive scoring systems. By aligning with human preferences, it enhances the immersive quality and overall quality of generated audio. This approach offers theoretical innovation in academia and more efficient audio generation solutions for the industry.
Technical Contribution
V2A-DPO introduces the AudioScore scoring system and curriculum learning strategy, significantly enhancing the quality and consistency of generated audio. Compared to existing DDPO methods, V2A-DPO achieves more efficient preference alignment and optimization in flow matching models. This framework provides new theoretical guarantees and engineering possibilities for video-to-audio generation.
Novelty
V2A-DPO is the first framework to apply direct preference optimization to flow-based video-to-audio generation. Compared to existing methods, V2A-DPO achieves more efficient human preference alignment through a comprehensive scoring system and curriculum learning strategy, significantly enhancing the quality and consistency of generated audio.
Limitations
- V2A-DPO's style control remains limited when dealing with scenarios significantly different from the training data.
- Quantitative assessment of aesthetic quality remains challenging, potentially affecting the immersive quality of audio.
- The model may require higher computational resources when handling complex preference pairs.
Future Work
Future research could explore more efficient methods for aesthetic quality assessment and style control in different scenarios. Further optimization of the curriculum learning strategy could enhance model performance on complex preference pairs.
AI Executive Summary
Video-to-audio generation (V2A) aims to synthesize high-quality audio aligned with video features. However, existing models face limitations in style control, aesthetic quality assessment, and comprehensive scoring systems. The V2A-DPO framework introduces the AudioScore system and curriculum learning strategy, significantly enhancing the quality and consistency of generated audio.
The V2A-DPO framework integrates the AudioScore system, an automated preference pair generation pipeline, and a curriculum learning optimization strategy. AudioScore evaluates semantic consistency, temporal alignment, and perceptual quality of generated audio. By combining a small set of human-annotated preference pairs with automatically generated ones, a large dataset is formed for optimization. The curriculum learning strategy optimizes the model in stages, enhancing the quality and consistency of generated audio.
Experimental results show that V2A-DPO optimized MMAudio outperforms DDPO-optimized and pre-trained models in audio quality and semantic alignment, with IS increasing by 1.81, IB-score by 0.86, and DeSync decreasing by 0.09. This method is significant in the video-to-audio generation field, addressing existing models' limitations in style control, aesthetic quality assessment, and comprehensive scoring systems.
Deep Dive
Abstract
This paper introduces V2A-DPO, a novel Direct Preference Optimization (DPO) framework tailored for flow-based video-to-audio generation (V2A) models, incorporating key adaptations to effectively align generated audio with human preferences. Our approach incorporates three core innovations: (1) AudioScore-a comprehensive human preference-aligned scoring system for assessing semantic consistency, temporal alignment, and perceptual quality of synthesized audio; (2) an automated AudioScore-driven pipeline for generating large-scale preference pair data for DPO optimization; (3) a curriculum learning-empowered DPO optimization strategy specifically tailored for flow-based generative models. Experiments on benchmark VGGSound dataset demonstrate that human-preference aligned Frieren and MMAudio using V2A-DPO outperform their counterparts optimized using Denoising Diffusion Policy Optimization (DDPO) as well as pre-trained baselines. Furthermore, our DPO-optimized MMAudio achieves state-of-the-art performance across multiple metrics, surpassing published V2A models.