Update README.md

README.md

@@ -14,11 +14,25 @@ pretty_name: VGGSynth1
 ---
 
 # VGGSynth1: Synthetic Audio-Visual Dataset (Part 1)
-### How Far Can We Go With Synthetic Data for Audio-Visual Sound Source Localization? (CVPR 2026 Highlight)
+## How Far Can We Go With Synthetic Data for Audio-Visual Sound Source Localization? (CVPR 2026 Highlight)
 
-This is the first part of the **VGGSynth** series, featuring high-fidelity synthetic pairs generated for sound source localization research. 
+### **Authors**
+**Arda Senocak\*, Sooyoung Park\*, Tae-Hyun Oh, Joon Son Chung** *(\* Equal Contribution)*
+
+---
+
+## **Introduction**
+**VGGSynth1** 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).
 
-- **Status:** CVPR 2026 Highlight
-- **Total Size:** ~140 GB
 - **Visuals:** Generated via **Stable Diffusion 3 (SD3)**
-- **Audio:** Generated via **Stable Audio**
+- **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}
+}