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arXiv · 2603.11089

V2A-DPO: Omni-Preference Optimization for Video-to-Audio Generation

Nolan Chan·Timmy Gang·Yongqian Wang·Yuzhe Liang·Dingdong Wang
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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.

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Nolan Chan, Timmy Gang, Yongqian Wang, Yuzhe Liang, Dingdong Wang. 2026-03-11. V2A-DPO: Omni-Preference Optimization for Video-to-Audio Generation. https://arxiv.org/abs/2603.11089

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