Update README.md

README.md

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 ---
 
 # OpenVTON
-
 A large-scale virtual try-on dataset containing ~100K clothing image pairs with garment masks.
+You
 
 ## Dataset Structure
 
@@ -62,3 +62,49 @@ sample["target"].show()  # person wearing garment
 print(sample["caption"])
 print(sample["category"])
 ```
+## Benchmark and Paper
+
+This dataset is part of **OpenVTON-Bench**, a large-scale benchmark designed for the systematic evaluation of controllable virtual try-on (VTON) models.
+
+**OpenVTON-Bench** is introduced in our paper:
+
+> **OpenVTON-Bench: A Large-Scale Benchmark for Controllable Virtual Try-On**
+> 📄 Paper: [https://arxiv.org/abs/2601.22725](https://arxiv.org/abs/2601.22725)
+> 💻 Code: [https://github.com/RenxingIntelligence/OpenVTON-Bench](https://github.com/RenxingIntelligence/OpenVTON-Bench)
+
+OpenVTON-Bench provides a standardized evaluation protocol for modern diffusion-based and transformer-based virtual try-on systems, enabling fair and reproducible comparison across different architectures.
+
+---
+
+## About OpenVTON-Bench
+
+**OpenVTON-Bench** is a **large-scale, high-resolution benchmark** designed for the **systematic evaluation of controllable virtual try-on models**.
+
+Unlike existing datasets and evaluation protocols that struggle with texture details and semantic consistency, OpenVTON-Bench provides:
+
+* 🖼️ **~100K Image Pairs** with resolutions up to **1536×1536**, enabling evaluation of fine-grained texture generation.
+* 🏷️ **Fine-Grained Taxonomy** covering **20 garment categories** for balanced semantic evaluation.
+* 📐 **Multi-Level Automated Evaluation**, including:
+
+  * Pixel fidelity
+  * Garment consistency
+  * Semantic realism
+
+This benchmark enables **fair, reproducible, and scalable comparison** across modern virtual try-on systems.
+
+---
+
+## Citation
+```bibtex
+If you use this dataset or the benchmark in your research, please cite:
+@misc{li2026openvtonbenchlargescalehighresolutionbenchmark,
+      title={OpenVTON-Bench: A Large-Scale High-Resolution Benchmark for Controllable Virtual Try-On Evaluation}, 
+      author={Jin Li and Tao Chen and Shuai Jiang and Weijie Wang and Jingwen Luo and Chenhui Wu},
+      year={2026},
+      eprint={2601.22725},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2601.22725}, 
+}
+```
+