NguyenDinhHieu/VITON-Extends-DB

VITON-Extends — Train & Test Data

VITON-Extends is an image-based virtual try-on dataset built on VITON with additional challenging poses from DeepFashion, as described in:

Enhancing Pose Adaptability in Virtual Try-On Systems
Nguyen Dinh Hieu (ORCID:0009-0002-6683-8036) , Tran Minh Khuong, Phan Duy Hung (ORCID: 0000-0002-6033-6484)
FPT University, Hanoi, Vietnam

• Paper (Springer LNCS, IUKM 2025): https://doi.org/10.1007/978-981-96-4606-7_21
• Code repository: https://github.com/nguyendinhhieu1309/VITON-Extends
• Model weights (Hugging Face): NguyenDinhHieu/VITON-Extends

Abstract

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.

Keywords: virtual try-on, pose adaptability, garment warping, StyleGAN, global appearance flow estimation, VITON benchmark.

Dataset summary

Property	Value
Focus	Upper-body virtual try-on (women), multi-pose
Typical resolution	1024 × 768
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
Annotations	Parsing-style labels, edges, DensePose-related assets (see folder layout)

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.

Folder layout (this upload)

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.

Inside each folder, subfolder names match what the 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/).

Under train/

Directory	Role
train/train_img/	Person / scene RGB images used for training
train/train_color/	Color-aligned representations (dataset-specific preprocessing)
train/train_edge/	Edge maps for garment / boundary cues
train/train_label/	Semantic parsing / label maps
train/train_densepose/	DensePose-related maps aligned with training images

Under test/

Directory	Role
test/test_img/	Person or test-scene RGB images
test/test_clothes/	Garment / clothing images for try-on
test/test_edge/	Edge maps aligned with the test setup

File naming follows the conventions expected by the training and testing scripts in the official code repository.

Intended use

• Training and evaluating parser-based and parser-free virtual try-on models that expect VITON-style directory layout.
• Research on pose-adaptive try-on and appearance-flow warping.

Not for: identifying individuals; any deployment that violates privacy or terms of the underlying source datasets.

How to load (example)

from huggingface_hub import snapshot_download

# Downloads repo root (including Train.zip and Test.zip) into ./VITON-Extends_data
path = snapshot_download(
    repo_id="NguyenDinhHieu/VITON-Extends-DB",
    repo_type="dataset",
    local_dir="./VITON-Extends_data",
)

# Then unzip Train.zip and Test.zip in that folder (Explorer, unzip, 7-Zip, etc.)
# so you get ./VITON-Extends_data/train/... and ./VITON-Extends_data/test/...

# Training: set dataroot to the train folder, e.g. ./VITON-Extends_data/train
# Testing:  set dataroot to the test folder,  e.g. ./VITON-Extends_data/test

# Only the training archive (smaller download):
# snapshot_download(..., allow_patterns=["Train.zip"])

# Only the test archive:
# snapshot_download(..., allow_patterns=["Test.zip"])

Use the train directory as dataroot when running training scripts, and the test directory as dataroot when running inference, per training / testing instructions.

Citation

If you use this dataset, please cite the paper:

@inproceedings{hieu2025vitonextends,
  title     = {Enhancing Pose Adaptability in Virtual Try-On Systems},
  author    = {Hieu, Nguyen Dinh and Khuong, Tran Minh and Hung, Phan Duy},
  booktitle = {Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2025)},
  series    = {Lecture Notes in Computer Science},
  volume    = {15585},
  publisher = {Springer},
  address   = {Singapore},
  year      = {2025},
  doi       = {10.1007/978-981-96-4606-7_21}
}

Acknowledgements

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 for full acknowledgements.

Contact

• hieundhe180318@fpt.edu.vn
• khuongtmhe180089@fpt.edu.vn
• hungpd2@fe.edu.vn

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Dataset card (Tiếng Việt — tóm tắt)

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. Vui lòng trích dẫn bài báo qua DOI ở trên.