# Video-to-Audio Generation with Fine-grained Temporal Semantics

arXiv: https://arxiv.org/abs/2409.14709 · HTML: https://mlsanity.com/paper/2409.14709/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2024.

## Built on by (most cited first, 6 of 6)

- [JavisDiT: Joint Audio-Video Diffusion Transformer with Hierarchical Spatio-Temporal Prior Synchronization](https://mlsanity.com/paper/2503.23377/) (2025)
- [Native Audio-Visual Alignment for Generation](https://mlsanity.com/paper/2605.30073/) (2026)
- [AGAV-Rater: Adapting Large Multimodal Model for AI-Generated Audio-Visual Quality Assessment](https://mlsanity.com/paper/2501.18314/) (2025)
- [Schrodinger Audio-Visual Editor: Object-Level Audiovisual Removal](https://mlsanity.com/paper/2512.12875/) (2025)
- [FoleyDirector: Fine-Grained Temporal Steering for Video-to-Audio Generation via Structured Scripts](https://mlsanity.com/paper/2603.19857/) (2026)
- [Beyond Time Shifts: Adapting Omni-LLM as a Reference-Free Evaluator for Generative Audio-Visual Models](https://mlsanity.com/paper/2607.09091/) (2026)

## Abstract (arXiv)

With recent advances of AIGC, video generation have gained a surge of research interest in both academia and industry (e.g., Sora). However, it remains a challenge to produce temporally aligned audio to synchronize the generated video, considering the complicated semantic information included in the latter. In this work, inspired by the recent success of text-to-audio (TTA) generation, we first investigate the video-to-audio (VTA) generation framework based on latent diffusion model (LDM). Similar to latest pioneering exploration in VTA, our preliminary results also show great potentials of LDM in VTA task, but it still suffers from sub-optimal temporal alignment. To this end, we propose to enhance the temporal alignment of VTA with frame-level semantic information. With the recently popular grounding segment anything model (Grounding SAM), we can extract the fine-grained semantics in video frames to enable VTA to produce better-aligned audio signal. Extensive experiments demonstrate the effectiveness of our system on both objective and subjective evaluation metrics, which shows both better audio quality and fine-grained temporal alignment.