---
license: cc-by-4.0
task_categories:
- other
tags:
- audio-visual-sound-source-localization
- multimodal-data
- synthetic-data
- multi-modal
- cvpr-2026
- stable-diffusion-3
- stable-audio
pretty_name: VGGSynth1
---

# VGGSynth1: Synthetic Audio-Visual Dataset (Part 2)
## How Far Can We Go With Synthetic Data for Audio-Visual Sound Source Localization? (CVPR 2026 Highlight)

### **Authors**
**Arda Senocak\*, Sooyoung Park\*, Tae-Hyun Oh, Joon Son Chung** *(\* Equal Contribution)*

---
## **Introduction**
**VGGSynth2** is a high-fidelity **synthetic clone of the VGGSound dataset**, built using state-of-the-art generative models. 
This dataset is designed to explore the boundaries and utility of synthetic data in training models for Audio-Visual Sound Source Localization (SSL).

- **Visuals:** Generated via **Stable Diffusion 3 (SD3)**
- **Audio:** Generated via **Stable Audio**
## **Citation**
```bibtex
@inproceedings{senocak2026howfar,
  title={How Far Can We Go With Synthetic Data for Audio-Visual Sound Source Localization?},
  author={Senocak, Arda and Park, Sooyoung and Oh, Tae-Hyun and Chung, Joon Son},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2026}
}