Step-by-Step Video-to-Audio Synthesis via Negative Audio Guidance
Not on the map. Why?
Abstract (arXiv)
We propose a step-by-step video-to-audio (V2A) generation method that provides finer control over the generation process and more realistic audio synthesis. Inspired by traditional Foley workflows, our approach enables incremental generation of complementary sounds, allowing users to author multiple sound events induced by a video. To avoid the need for costly multi-reference video-audio datasets, each generation step is formulated as a negatively guided V2A process that discourages duplication of sounds already present in previously generated tracks. The guidance model is trained by finetuning a pre-trained V2A model on audio pairs from non-overlapping segments of the same video, encouraging it to leverage acoustic context while remaining visually grounded, and enabling training with standard single-reference audiovisual datasets. Objective and subjective evaluations demonstrate that our method enhances the separability of generated sounds at each step and improves the overall quality of the final composite audio, outperforming existing baselines. Our project page is available at: https://ahykw.github.io/sbsv2a/.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 48 of its 64 references that are in the map. First posted on arXiv 26 Jun 2025.
Builds on
48 of 64 references.
References · 48
- 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
- 2022Classifier-Free Diffusion Guidancecited by 5,847
- 2022Flow Matching for Generative Modelingcited by 5,702
- 2025Qwen2.5-VL Technical Reportcited by 5,587
- 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
- 2024Scaling Rectified Flow Transformers for High-Resolution Image Synthesiscited by 4,181
- 2016Improved Techniques for Training GANscited by 2,970
- 2023ImageBind: One Embedding Space To Bind Them Allcited by 1,175
- 2022Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentationcited by 719
- 2020VGGSound: A Large-scale Audio-Visual Datasetcited by 648
- 2024Movie Gen: A Cast of Media Foundation Modelscited by 644
- 2022Compositional Visual Generation with Composable Diffusion Modelscited by 624
36 more
- 2019PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognitioncited by 574
- 2023Analyzing and Improving the Training Dynamics of Diffusion Modelscited by 402
- 2019Clotho: An Audio Captioning Datasetcited by 358
- 2018Audio-Visual Scene Analysis with Self-Supervised Multisensory Featurescited by 335
- 2023WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Researchcited by 267
- 2021Taming Visually Guided Sound Generationcited by 136
- 2023Diff-Foley: Synchronized Video-to-Audio Synthesis with Latent Diffusion Modelscited by 134
- 2024MMAudio: Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesiscited by 134
- 2024Seeing and Hearing: Open-domain Visual-Audio Generation with Diffusion Latent Alignerscited by 111
- 2024FoleyCrafter: Bring Silent Videos to Life with Lifelike and Synchronized Soundscited by 110
- 2024Synchformer: Efficient Synchronization from Sparse Cuescited by 97
- 2023AUDIT: Audio Editing by Following Instructions with Latent Diffusion Modelscited by 83
- 2020Generating Visually Aligned Sound from Videoscited by 67
- 2023V2A-Mapper: A Lightweight Solution for Vision-to-Audio Generation by Connecting Foundation Modelscited by 65
- 2023Multi-Source Diffusion Models for Simultaneous Music Generation and Separationcited by 63
- 2024Frieren: Efficient Video-to-Audio Generation Network with Rectified Flow Matchingcited by 53
- 2024Temporally Aligned Audio for Video with Autoregressioncited by 52
- 2024Video-Guided Foley Sound Generation with Multimodal Controlscited by 49
- 2024Read, Watch and Scream! Sound Generation from Text and Videocited by 48
- 2022Separate What You Describe: Language-Queried Audio Source Separationcited by 45
- 2023Separate Anything You Describecited by 43
- 2024Add-it: Training-Free Object Insertion in Images With Pretrained Diffusion Modelscited by 43
- 2025Kling-Foley: Multimodal Diffusion Transformer for High-Quality Video-to-Audio Generationcited by 40
- 2024Tell What You Hear From What You See -- Video to Audio Generation Through Textcited by 34
- 2023StemGen: A music generation model that listenscited by 32
- 2025AudioX: A Unified Framework for Anything-to-Audio Generationcited by 27
- 2024Sketch2Sound: Controllable Audio Generation via Time-Varying Signals and Sonic Imitationscited by 24
- 2023InstructME: An Instruction Guided Music Edit And Remix Framework with Latent Diffusion Modelscited by 23
- 2024Action2Sound: Ambient-Aware Generation of Action Sounds from Egocentric Videoscited by 21
- 2023SmartMask: Context Aware High-Fidelity Mask Generation for Fine-grained Object Insertion and Layout Controlcited by 18
- 2024VinTAGe: Joint Video and Text Conditioning for Holistic Audio Generationcited by 16
- 2020Visually Guided Sound Source Separation using Cascaded Opponent Filter Networkcited by 15
- 2024Generalized Multi-Source Inference for Text Conditioned Music Diffusion Modelscited by 13
- 2024Simultaneous Music Separation and Generation Using Multi-Track Latent Diffusion Modelscited by 13
- 2024AudioEditor: A Training-Free Diffusion-Based Audio Editing Frameworkcited by 13
- 2023Visually-Guided Sound Source Separation with Audio-Visual Predictive Codingcited by 2
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.