Core Viewpoint - The article introduces VAFlow, a novel framework for video-to-audio generation that directly models the mapping from video to audio, overcoming limitations of traditional methods that rely on noise-based priors [6][9][29]. Background - The transition from "noise to sound" to "video to sound" highlights the evolution in multimodal generation tasks, particularly in video-to-audio (V2A) generation [3]. Traditional Methods - Early V2A methods utilized autoregressive and mask-prediction approaches, which faced challenges due to the discrete representation of audio leading to quality limitations [4][5]. VAFlow Framework - VAFlow eliminates the dependency on Gaussian noise priors, enabling direct generation of audio from video distributions, resulting in significant improvements in generation quality, semantic alignment, and synchronization accuracy [6][8][9]. Comparison of Generation Paradigms - The article contrasts traditional diffusion models and flow matching methods with VAFlow, demonstrating that VAFlow achieves better performance in terms of convergence speed and audio quality metrics [19][20]. Prior Analysis - The study compares Gaussian prior and video prior, showing that video prior offers better alignment with audio latent space, leading to superior generation quality [12][15]. Performance Metrics - VAFlow outperforms existing state-of-the-art (SOTA) methods in audio generation quality metrics, achieving the best scores in various benchmarks without complex video conditioning modules [24][25]. Visual Results - The article presents visual comparisons of generated audio from VAFlow against ground truth, illustrating its capability to accurately interpret complex scenes and maintain audio-visual synchronization [27]. Future Directions - The research team plans to explore VAFlow's applications in broader audio domains, including speech and music, indicating its potential for general multimodal generation [29].