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Video-to-audio synthesis

Generates synchronized soundtracks from video with latent diffusion and text conditioning.

76 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2017–2026) · an approach in Video generation, keyword cluster “audio · editing · talking”

State History Tried Checklist Preprints Who Export

Where it stands

Not enough benchmark data Fewer than four papers report the same benchmark, so there is no record to follow.

11 papers in 2023–24 and 58 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic held steady, from 4.8% to 5.2%.

How this is decided

A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.

How it got here

2016: 0162017: 1172018: 0182019: 0192020: 1202021: 2212022: 3222023: 6232024: 5242025: 22252026: 3626

Most cited paper of each year

Earliest papers here

What has been tried

37 papers here report 407 measured changes (a component added, removed or swapped, compared with a baseline): helped 72%, no clear effect 3%, hurt 22%, mixed 3%. Papers mostly publish changes that helped, so "helped" is over-represented.

Ablations in recent papers

What was changedMeasured onResultEffectPaper
Average all audio components into a single conditioning vector rather than using temporal windowsVGGSound · AV-Align0.35 -> 0.29HurtDiverse and Aligned Audio-to-Video Generation via Text-to-Video Model Adaptation (AAAI 2024 · 35 citations)
jointly train on audio-visual-text and audio-text dataVGGSound test · FD_PaSST77.38 -> 70.19HelpedMMAudio: Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis (CVPR 2025 · 29 citations)
remove long-term modelingV2M-bench · KL0.738 -> 0.898HurtVidMuse: A Simple Video-to-Music Generation Framework with Long-Short-Term Modeling (CVPR 2025 · 18 citations)
combine CLIP and I3D Flow visual featuresMV100K · MCC0.343/0.419HelpedV2Meow: Meowing to the Visual Beat via Video-to-Music Generation (AAAI 2024 · 18 citations)
Add negative promptingVGGSound-sync text-control evaluation · CLAP accuracy85.5 -> 93.2HelpedVideo-Guided Foley Sound Generation with Multimodal Controls (CVPR 2025 · 16 citations)
replace estimated video energy with ground-truth audio energyVGGSound · FD15.24 -> 13.93HelpedRead, Watch and Scream! Sound Generation from Text and Video (AAAI 2025 · 14 citations)
Add the LLM converter to audio-relevant visual conditioningVGGSound test · KLD2.43 -> 2.25HelpedTell What You Hear From What You See - Video to Audio Generation Through Text (NeurIPS 2024 · 11 citations)
use reflow for 1-step samplingVGGSound test, 1 step · Acc (%)43.18 -> 94.96HelpedFrieren: Efficient Video-to-Audio Generation Network with Rectified Flow Matching (NeurIPS 2024 · 8 citations)
Replace ViT with ViViT for visual feature extractionAblation test set · KLD1.07 -> 1.25HurtGVMGen: A General Video-to-Music Generation Model with Hierarchical Attentions (AAAI 2025 · 7 citations)
add the BPS networkAIST++ · BAS0.22 -> 0.33HelpedDanceAnyWay: Synthesizing Beat-Guided 3D Dances with Randomized Temporal Contrastive Learning (AAAI 2024 · 6 citations)
add teacher guidance lossVinTAGe-Bench · FID19.48 -> 16.43HelpedVinTAGe: Joint Video and Text Conditioning for Holistic Audio Generation (CVPR 2025 · 5 citations)
Add R2-DiT refinementCombined test set · Hand-C0.8579 -> 0.8832HelpedAudCast: Audio-Driven Human Video Generation by Cascaded Diffusion Transformers (CVPR 2025 · 4 citations)

What reviewers will expect

Datasets and baselines named in the 27 papers of this approach from 2025–2026 that have a full-text summary. Leaving out the common ones is a frequent review question.

Datasets

  • VGGSound · 11 of 27 papers
  • Greatest Hits · 4 of 27 papers
  • AIST++ · 3 of 27 papers
  • SymMV · 2 of 27 papers
  • TikTok · 2 of 27 papers
  • AVSync15 · 2 of 27 papers
  • Landscape · 2 of 27 papers
  • AudioSet · 2 of 27 papers

Baselines

  • Diff-Foley · 9 of 27 papers
  • SpecVQGAN · 6 of 27 papers
  • Im2Wav · 5 of 27 papers
  • V2A-Mapper · 4 of 27 papers
  • FoleyCrafter · 4 of 27 papers
  • CoDi · 3 of 27 papers
  • Seeing&Hearing · 3 of 27 papers
  • Frieren · 3 of 27 papers

Recent preprints

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    Who publishes on it

    Institutions on the most papers of this approach since 2023 (of 69 papers; from OpenAlex and the papers' affiliations).

    Use it

    Download BibTeX of 31 key papersOpen the topic in the interactive map

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    Same keyword cluster

    Elsewhere in the topic

    Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).

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