--- title: Tstars-VTON canonical_url: "https://www.modelscope.cn/datasets/TaoTianGroup/Tstars-VTON" md_url: "https://www.modelscope.cn/datasets/TaoTianGroup/Tstars-VTON.md" repository: TaoTianGroup/Tstars-VTON chinese_name: "淘宝星辰试衣评测集" last_updated: 2026-04-22 license: CC-BY-4.0 storage_size: "6.8 GB" downloads: 871 stars: 3 --- # Tstars-VTON > Tstars-VTON - TaoTianGroup 在 ModelScope 开源的数据集。数据集文件元信息以及数据文件，请浏览“数据集文件”页面获取。 TaoTianGroup/Tstars-VTON 是 ModelScope 魔搭社区上的数据集，存储大小 6.8 GB，采用 CC-BY-4.0 许可。 - **Repository**: TaoTianGroup/Tstars-VTON - **License**: CC-BY-4.0 - **Storage size**: 6.8 GB - **Downloads**: 871 - **Stars**: 3 - **Last updated**: 2026-04-22 Source: https://www.modelscope.cn/datasets/TaoTianGroup/Tstars-VTON --- 数据集文件元信息以及数据文件，请浏览“数据集文件”页面获取。 当前数据集卡片使用的是默认模版，数据集的贡献者未提供更加详细的数据集介绍，但是您可以通过如下GIT Clone命令，或者ModelScope SDK来下载数据集 #### 下载方法 :modelscope-code[]{type="sdk"} :modelscope-code[]{type="git"} # Tstars-Tryon 1.0

Hugging Face Dataset ModelScope Dataset Technical Report License

## Commercial Applications Our virtual try-on model, Tstars-Tryon 1.0, is now deployed on the Taobao App. Simply scan the QR code below with the Taobao app to instantly try on your favorite looks. We hope you enjoy a seamless and delightful shopping experience! ![图片描述](./Evaluation_Toolkit/assets/qrcode.png) ## Tstars-VTON - MetaInfo ### Introduction Tstars-VTON is a comprehensive benchmark designed to evaluate whether a virtual try-on model is truly capable of functioning in real-world scenarios. In total, Tstars-VTON comprises 1780 random paired samples across 5 garment categories(up, coat, pant, skirt, dress) and 3 accessory categories(shoes, bag, hat), covering a diverse range of 465 fine-grained subcategories. ## ✨ Key Features - **High-Complexity Multi-Garment/Accessory Try-On Scenarios**: Introduces multi-garment combinations with layered outfits and accessories, covering free-combination scenarios with 1-6 items. - **Diverse Data Coverage and Fine-Grained Attributes**: Fine-gined attributes of both model and garment images through VLM-based generation and manual check. - **Privacy-Preserving Mechanism**: All portraits collected from open-source data are matched to a similar face in licensed database and anonymized through face swapping. - **Flexible Unpaired Settings**: Supports a fully unpaired evaluation setting, decouplin the model and garment database - **Comprehensive Evaluation Paradigm Aligned with Human Preferences**: Proposes a VLM-driven evaluation paradigm that Decomposes virtual try-on quality into five rigorous dimensions. ## ✨ Key Attributes Key attributes in our Tstars-VTON | Fields | Description | |----------|-------| | model | Reference image of the person (target identity) to be dressed | | up | Source image of the upper-body garment (e.g., T-shirt, blouse) | | coat | Source image of the outerwear garment (e.g., jacket, coat) | | pant | Source image of the lower-body garment (e.g., trousers, jeans) | | skirt | Source image of the lower-body garment (skirt) | | dress | Source image of the full-body garment (one-piece dress) | | shoes | Source image of shoes (e.g., Sneakers, High Heels) | | bag | Source image of bags (e.g., handbag, shoulder bag) | | hat | Source image of hats (e.g., Peaked Cap, Beret) | | short_caption | English short instruction generated by gemini-3.1-flash-lite-preview | | model_info | Model attributes（VLM-based + manual check）: Gender, Age, Skin_tone, Body_type, Pose, Clothing_occlusion, Lighting_condition, Shooting_angle, Background_complexity, Clarity, Portrait_size | | up_info | Cloth attributes（VLM-based + manual check）: Clothing-Category, Clothing-Style, Clothing-Length, Clothing-Sleeve_length, Clothing-Fit, Clothing-Material, Clothing-Texture, Clothing-Design_detail, Clothing-Display_format, Gender_fit | | coat_info | Same as up_info | | pant_info | Same as up_info | | skirt_info | Same as up_info | | dress_info | Same as up_info | | shoes_info | Accessory attributes（VLM-based + manual check）: Accessory-Category, Accessory-Style, Accessory-Display_format, Gender_fit | | bag_info | Same as shoes_info | | hat_info | Same as shoes_info | ### Distribution The Distribution of Multi-Garment/Accessory Try-On Combinations | Garment nums | Count | |----------|-------| | 1 | 799 | | 2 | 320 | | 3 | 286 | | 4 | 173 | | 5 | 104 | | 6 | 98 | ## Tstars-VTON - Evaluation Toolkit An automated evaluation toolkit for virtual try-on models, powered by VLM-as-Judge (e.g., Gemini). Given a set of try-on results, the toolkit scores each sample across **four quality dimensions** and produces an aggregated report. ### Scoring Dimensions Each dimension is scored on a **1.0–10.0** scale: | Dimension | What it measures | |---|---| | **identity_consistency** | Face, body shape, pose, scale and skin tone preservation | | **garment_fidelity** | Silhouette, style details, color, pattern and layering correctness | | **background_preservation** | Background content unchanged, no cropping or expansion | | **physical_logic** | Limb anatomy, mesh clipping and object interpenetration artifacts | The **overall score** is the geometric mean of the four dimension scores. ### How It Works The toolkit uses a **split-call strategy** — each sample is evaluated via two VLM API calls: - **Call 1** (identity + garment): sends `[person, garment(s)..., result]` images - **Call 2** (background + physics): sends `[person, result]` images This split design improves scoring accuracy by letting the VLM focus on related aspects together. ### Prerequisites - **Benchmark dataset**: the Tstars-VTON Benchmark parquet files (`Tstars-VTON-*.parquet`) - **API access**: an OpenAI-compatible VLM endpoint (default: Gemini API) ### Input Format Prepare a **JSONL file** where each line maps a benchmark sample index to your model's try-on result image: ```jsonl {"sample_index": 0, "result": "/path/to/result_0.png"} {"sample_index": 1, "result": "/path/to/result_1.png"} {"sample_index": 2, "result": "/path/to/result_2.png"} ``` - `sample_index`: the 0-based index into the benchmark dataset - `result`: path to the generated try-on image ### Usage ```bash python Evaluation_Toolkit/eval.py \ --dataset_path "/path/to/Tstars-VTON-*.parquet" \ --result_jsonl "/path/to/my_results.jsonl" \ --output_dir eval_output/my_model \ --api_key "YOUR_API_KEY" \ --workers 8 ``` ### Output The toolkit produces two files in `--output_dir`: **`cases.jsonl`** — per-sample scoring details **`summary.json`** — aggregated scores with breakdowns Results are broken down into **overall**, **single-garment** (1 item), and **multi-garment** (2+ items) subsets. ### Features - **Resume support**: if evaluation is interrupted, re-running the same command will skip already-scored samples and continue from where it left off. - **Multi-key rotation**: pass multiple API keys (comma-separated) to distribute requests across keys and avoid rate limits. - **Concurrent scoring**: use `--workers` to control parallelism for faster evaluation. - **Custom VLM endpoint**: any OpenAI-compatible API can be used via `--api_base_url` and `--model_name`. ### File Structure ``` Evaluation_Toolkit/ ├── assets/ │ └── sample_index0.png ├── eval.py # Main evaluation script ├── tryon_prompts.py # VLM prompt templates for all scoring dimensions ├── run.sh # Example run script └── test.jsonl # Test file ``` ## License **Tstars-VTON** is released under the [Creative Commons Attribution–NonCommercial–NoDerivatives (CC BY-NC-ND 4.0)](https://creativecommons.org/licenses/by-nc-nd/4.0/) license. - ✅ **Free for academic research purposes only** - ❌ **Commercial use is prohibited** **Data Source:** All images included in Tstars-VTON were legally purchased and obtained through official channels to ensure copyright compliance. *By using this dataset, you agree to comply with the applicable license terms.* ## 🖊️ Citation We kindly encourage citation of our work if you find it useful. ```bibtex @misc{chen2026tstarstryon10robustrealistic, title={Tstars-Tryon 1.0: Robust and Realistic Virtual Try-On for Diverse Fashion Items}, author={Mengting Chen and Zhengrui Chen and Yongchao Du and Zuan Gao and Taihang Hu and Jinsong Lan and Chao Lin and Yefeng Shen and Xingjian Wang and Zhao Wang and Zhengtao Wu and Xiaoli Xu and Zhengze Xu and Hao Yan and Mingzhou Zhang and Jun Zheng and Qinye Zhou and Xiaoyong Zhu and Bo Zheng}, year={2026}, eprint={2604.19748}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2604.19748}, } ```