Datasets:
license: other
task_categories:
- image-to-image
language:
- en
tags:
- virtual-try-on
- fashion
- image-synthesis
- viton
- pose-adaptability
- appearance-flow
pretty_name: VITON-Extends (Train + Test Data)
size_categories:
- 10K<n<100K
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 top-level folders: train/ (training assets) and test/ (evaluation assets); see 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 repository root contains train/ and test/. 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 snapshot_download 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
# Full snapshot: creates ./VITON-Extends_data/train/... and ./VITON-Extends_data/test/...
path = snapshot_download(
repo_id="NguyenDinhHieu/VITON-Extends-DB",
repo_type="dataset",
local_dir="./VITON-Extends_data",
)
# 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 training split:
# snapshot_download(..., allow_patterns=["train/**"])
# Only test split:
# snapshot_download(..., allow_patterns=["test/**"])
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
Dataset card (Tiếng Việt — tóm tắt)
Repo trên Hub có hai thư mục gốc train/ và test/. Trong train/ là các thư mục con train_img, train_color, train_edge, train_label, train_densepose; trong test/ là 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.