# LD-LAudio-V1: Video-to-Long-Form-Audio Generation Extension with Dual Lightweight Adapters

arXiv: https://arxiv.org/abs/2508.11074 · HTML: https://mlsanity.com/paper/2508.11074/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 21 references that are in the map.
- First posted on arXiv 14 Aug 2025.

## Builds on (most cited first, 18 of 18)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [VGGSound: A Large-scale Audio-Visual Dataset](https://mlsanity.com/paper/2004.14368/) (2020)
- [Taming Visually Guided Sound Generation](https://mlsanity.com/paper/2110.08791/) (2021)
- [Diff-Foley: Synchronized Video-to-Audio Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2306.17203/) (2023)
- [MMAudio: Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis](https://mlsanity.com/paper/2412.15322/) (2024)
- [FoleyCrafter: Bring Silent Videos to Life with Lifelike and Synchronized Sounds](https://mlsanity.com/paper/2407.01494/) (2024)
- [Synchformer: Efficient Synchronization from Sparse Cues](https://mlsanity.com/paper/2401.16423/) (2024)
- [Factorized Contrastive Learning: Going Beyond Multi-view Redundancy](https://mlsanity.com/paper/2306.05268/) (2023)
- [V2A-Mapper: A Lightweight Solution for Vision-to-Audio Generation by Connecting Foundation Models](https://mlsanity.com/paper/2308.09300/) (2023)
- [Temporally Aligned Audio for Video with Autoregression](https://mlsanity.com/paper/2409.13689/) (2024)
- [Video-Guided Foley Sound Generation with Multimodal Controls](https://mlsanity.com/paper/2411.17698/) (2024)
- [Dense-Localizing Audio-Visual Events in Untrimmed Videos: A Large-Scale Benchmark and Baseline](https://mlsanity.com/paper/2303.12930/) (2023)
- [FoleyGen: Visually-Guided Audio Generation](https://mlsanity.com/paper/2309.10537/) (2023)
- [SkyReels-Audio: Omni Audio-Conditioned Talking Portraits in Video Diffusion Transformers](https://mlsanity.com/paper/2506.00830/) (2025)
- [LoVA: Long-form Video-to-Audio Generation](https://mlsanity.com/paper/2409.15157/) (2024)
- [Tri-Ergon: Fine-grained Video-to-Audio Generation with Multi-modal Conditions and LUFS Control](https://mlsanity.com/paper/2412.20378/) (2024)
- [Long-Video Audio Synthesis with Multi-Agent Collaboration](https://mlsanity.com/paper/2503.10719/) (2025)
- [RFLAV: Rolling Flow matching for infinite Audio Video generation](https://mlsanity.com/paper/2503.08307/) (2025)

## Abstract (arXiv)

Generating high-quality and temporally synchronized audio from video content is essential for video editing and post-production tasks, enabling the creation of semantically aligned audio for silent videos. However, most existing approaches focus on short-form audio generation for video segments under 10 seconds or rely on noisy datasets for long-form video-to-audio zsynthesis. To address these limitations, we introduce LD-LAudio-V1, an extension of state-of-the-art video-to-audio models and it incorporates dual lightweight adapters to enable long-form audio generation. In addition, we release a clean and human-annotated video-to-audio dataset that contains pure sound effects without noise or artifacts. Our method significantly reduces splicing artifacts and temporal inconsistencies while maintaining computational efficiency. Compared to direct fine-tuning with short training videos, LD-LAudio-V1 achieves significant improvements across multiple metrics: FDpasst 450.00 rightarrow 327.29 (+27.27%), FDpanns 34.88 rightarrow 22.68 (+34.98%), FDvgg 3.75 rightarrow 1.28 (+65.87%), KLpanns 2.49 rightarrow 2.07 (+16.87%), KLpasst 1.78 rightarrow 1.53 (+14.04%), ISpanns 4.17 rightarrow 4.30 (+3.12%), IBscore 0.25 rightarrow 0.28 (+12.00%), EnergyDelta10ms 0.3013 rightarrow 0.1349 (+55.23%), EnergyDelta10ms(vs.GT) 0.0531 rightarrow 0.0288 (+45.76%), and Sem.,Rel. 2.73 rightarrow 3.28 (+20.15%). Our dataset aims to facilitate further research in long-form video-to-audio generation and is available at https://github.com/deepreasonings/long-form-video2audio.