OpenVTON

A large-scale virtual try-on dataset containing ~100K clothing image pairs with garment masks.

Dataset Structure

Each sample contains:

• source: Garment image (clothing item)
• mask: Garment segmentation mask
• target: Person wearing the garment (ground truth)
• caption: Text description of the clothing
• category: Clothing category (e.g., pants, jeans, shirt)

Splits

Split	Samples
Train	89,927
Validation	4,989
Test	5,009
Total	99,925

Usage

from datasets import load_dataset

dataset = load_dataset("RenxingIntelligence/OpenVTON")
sample = dataset["train"][0]
sample["source"].show()  # garment image
sample["mask"].show()    # segmentation mask
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 💻 Code: 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.

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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.

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Citation

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}, 
}