| ---
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| license: other
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| task_categories:
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| - image-to-image
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| language:
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| - en
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| tags:
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| - virtual-try-on
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| - fashion
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| - image-synthesis
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| - viton
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| - pose-adaptability
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| - appearance-flow
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| pretty_name: VITON-Extends (Train + Test Data)
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| size_categories:
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| - 10K<n<100K
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| ---
|
|
|
| # VITON-Extends — Train & Test Data
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|
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| **VITON-Extends** is an image-based virtual try-on dataset built on [VITON](https://github.com/shadow2496/VITON-HD) with additional challenging poses from [DeepFashion](http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html), as described in:
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| **Enhancing Pose Adaptability in Virtual Try-On Systems**
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| Nguyen Dinh Hieu (ORCID:[0009-0002-6683-8036](https://orcid.org/0009-0002-6683-8036))
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| , Tran Minh Khuong, Phan Duy Hung (ORCID: [0000-0002-6033-6484](https://orcid.org/0000-0002-6033-6484))
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| FPT University, Hanoi, Vietnam
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|
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| - **Paper (Springer LNCS, IUKM 2025):** [https://doi.org/10.1007/978-981-96-4606-7_21](https://doi.org/10.1007/978-981-96-4606-7_21)
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| - **Code repository:** [https://github.com/nguyendinhhieu1309/VITON-Extends](https://github.com/nguyendinhhieu1309/VITON-Extends)
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| - **Model weights (Hugging Face):** [NguyenDinhHieu/VITON-Extends](https://huggingface.co/NguyenDinhHieu/VITON-Extends)
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|
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| ## Abstract
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| Accurate garment fitting in virtual try-on systems remains difficult under complex body poses, occlusions, and large misalignments between person and garment images. The VITON-Extends line of work improves **pose adaptability** and **garment warping** using a **global appearance flow** estimator with **StyleGAN-style** global modulation and a **local flow refinement** stage. Experiments on the VITON benchmark show strong results, especially in challenging poses.
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| **Keywords:** virtual try-on, pose adaptability, garment warping, StyleGAN, global appearance flow estimation, VITON benchmark.
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|
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| ## Dataset summary
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|
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| | Property | Value |
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| |----------|--------|
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| | Focus | Upper-body virtual try-on (women), multi-pose |
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| | Typical resolution | 1024 × 768 |
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| | Splits on Hub | Two archives at repo root: **`Train.zip`** and **`Test.zip`**. **Extract both** after download to obtain the **`train/`** and **`test/`** folder layout below |
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| | Annotations | Parsing-style labels, edges, DensePose-related assets (see folder layout) |
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| VITON-Extends extends the original VITON setting with **more diverse standing postures and arm configurations** (e.g. arms crossed, sideways stance), which stress-tests warping and synthesis under occlusion and misalignment.
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| ## Folder layout (this upload)
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| The Hugging Face dataset repository root ships **`Train.zip`** and **`Test.zip`** (not the raw folders). **You must unzip both** into the same parent directory (e.g. your `local_dir` after `snapshot_download`) so you end up with **`train/`** and **`test/`** as described below. If an archive already contains a single top-level `train` or `test` folder, extract as usual; if your tool nests an extra directory, move the inner `train` / `test` so `dataroot` points at the folder that **directly** contains `train_img/`, `test_img/`, etc.
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| Inside each folder, subfolder names match what the [VITON-Extends](https://github.com/nguyendinhhieu1309/VITON-Extends) code expects **relative to `--dataroot`**: training reads `train_img`, `train_label`, … from the directory you pass as `dataroot`, so **after extracting**, point **`dataroot` to the `train` folder** (the path that directly contains `train_img/`, …). For testing / inference, point **`dataroot` to the `test` folder** (the path that directly contains `test_img/`, `test_clothes/`, `test_edge/`).
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| ### Under `train/`
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| | Directory | Role |
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| |-----------|------|
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| | `train/train_img/` | Person / scene RGB images used for training |
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| | `train/train_color/` | Color-aligned representations (dataset-specific preprocessing) |
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| | `train/train_edge/` | Edge maps for garment / boundary cues |
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| | `train/train_label/` | Semantic parsing / label maps |
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| | `train/train_densepose/` | DensePose-related maps aligned with training images |
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|
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| ### Under `test/`
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| | Directory | Role |
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| |-----------|------|
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| | `test/test_img/` | Person or test-scene RGB images |
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| | `test/test_clothes/` | Garment / clothing images for try-on |
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| | `test/test_edge/` | Edge maps aligned with the test setup |
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| File naming follows the conventions expected by the training and testing scripts in the official code repository.
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|
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| ## Intended use
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| - Training and evaluating **parser-based** and **parser-free** virtual try-on models that expect VITON-style directory layout.
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| - Research on **pose-adaptive** try-on and **appearance-flow** warping.
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| **Not for:** identifying individuals; any deployment that violates privacy or terms of the underlying source datasets.
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|
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| ## How to load (example)
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| ```python
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| from huggingface_hub import snapshot_download
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| # Downloads repo root (including Train.zip and Test.zip) into ./VITON-Extends_data
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| path = snapshot_download(
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| repo_id="NguyenDinhHieu/VITON-Extends-DB",
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| repo_type="dataset",
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| local_dir="./VITON-Extends_data",
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| )
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| # Then unzip Train.zip and Test.zip in that folder (Explorer, unzip, 7-Zip, etc.)
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| # so you get ./VITON-Extends_data/train/... and ./VITON-Extends_data/test/...
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| # Training: set dataroot to the train folder, e.g. ./VITON-Extends_data/train
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| # Testing: set dataroot to the test folder, e.g. ./VITON-Extends_data/test
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|
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| # Only the training archive (smaller download):
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| # snapshot_download(..., allow_patterns=["Train.zip"])
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|
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| # Only the test archive:
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| # snapshot_download(..., allow_patterns=["Test.zip"])
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| ```
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| Use the **`train`** directory as `dataroot` when running training scripts, and the **`test`** directory as `dataroot` when running inference, per [training / testing instructions](https://github.com/nguyendinhhieu1309/VITON-Extends).
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|
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| ## Citation
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| If you use this dataset, please cite the paper:
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|
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| ```bibtex
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| @inproceedings{hieu2025vitonextends,
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| title = {Enhancing Pose Adaptability in Virtual Try-On Systems},
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| author = {Hieu, Nguyen Dinh and Khuong, Tran Minh and Hung, Phan Duy},
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| booktitle = {Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2025)},
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| series = {Lecture Notes in Computer Science},
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| volume = {15585},
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| publisher = {Springer},
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| address = {Singapore},
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| year = {2025},
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| doi = {10.1007/978-981-96-4606-7_21}
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| }
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| ```
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|
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| ## Acknowledgements
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|
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| This dataset builds on **VITON** / community virtual try-on resources and **DeepFashion**-sourced challenging poses, following the methodology described in the paper. The implementation builds on ideas from **ClothFlow** and related appearance-flow try-on works; see the [GitHub repository](https://github.com/nguyendinhhieu1309/VITON-Extends) for full acknowledgements.
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|
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| ## Contact
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|
|
| - hieundhe180318@fpt.edu.vn
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| - khuongtmhe180089@fpt.edu.vn
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| - hungpd2@fe.edu.vn
|
|
|
| ---
|
|
|
| ### Dataset card (Tiếng Việt — tóm tắt)
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|
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| Trên Hub, dữ liệu chính nằm ở hai file nén gốc **`Train.zip`** và **`Test.zip`**. Sau khi tải về, **bắt buộc giải nén** cả hai để có thư mục **`train/`** và **`test/`**. Trong `train/` gồm các thư mục con `train_img`, `train_color`, `train_edge`, `train_label`, `train_densepose`; trong `test/` gồm `test_img`, `test_clothes`, `test_edge`. Khi chạy code, đặt `dataroot` trỏ vào thư mục **`train`** (huấn luyện) hoặc **`test`** (suy luận). Mô hình: [NguyenDinhHieu/VITON-Extends](https://huggingface.co/NguyenDinhHieu/VITON-Extends). Vui lòng trích dẫn bài báo qua DOI ở trên.
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