README.md

metadata

license: cc-by-4.0
task_categories:
  - 3D data rendering from 2D video
  - 3D semantic segmentation
  - 3D data reconstruction

🧵 3DFDReal: 3D Fashion Data from the Real World

ETRI Media Intellectualization Research Section, ETRI

3DFDReal is a real-world fashion dataset tailored for 3D vision tasks such as segmentation, reconstruction, rigging, and deployment in metaverse platforms. Captured from high-resolution multi-view 2D videos (4K @ 60fps), the dataset includes both individual fashion items and combined outfits worn by mannequins.

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🔍 Overview

3DFDReal bridges the gap between high-quality 3D fashion modeling and practical deployment in virtual environments, such as ZEPETO. It features over 1,000 3D point clouds, each enriched with detailed metadata including:

• Class labels
• Gender and pose type
• Texture and semantic attributes
• Structured segmentations

This dataset provides a foundation for advancing research in pose-aware 3D understanding, avatar modeling, and digital twin applications.

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🎥 Data Collection Pipeline

The dataset is built through a structured four-stage pipeline:

1. Asset Selection: Fashion items (e.g., shoes, tops, accessories) are selected and tagged individually or in sets.
2. Recording Setup: Items or mannequins are filmed using an iPhone 13 Pro from multi-view angles for 3D reconstruction.
3. 3D Ground Truth Generation: Videos are converted into colored point clouds and manually segmented using professional 3D labeling tools.
4. Application & Validation: Assets are rigged and tested in avatar environments like ZEPETO for deployment readiness.

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📊 Dataset Statistics

📈 Class Distribution

The most frequently appearing classes include pants, sweatshirts, and jeans.

👚 Combination Metadata

Key observations:

• Most mannequin outfits contain four distinct fashion items.
• Gender distribution is balanced across combinations.
• T-poses are selectively used for rigging, while upright poses dominate standard recordings.

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📁 Dataset Structure

dataset_root/
├── PointCloud_Asset/
├── Video_Asset/
├── Label_Asset/
├── PointCloud_Combine/
│   ├── train/
│   ├── val/
│   └── test/
├── Video_Combine/
│   ├── train/
│   ├── val/
│   └── test/
├── Label_Combine/
│   ├── train/
│   ├── val/
│   └── test/
└── meta/
    ├── asset_meta.json
    ├── combination_meta.json
    ├── train_combination_meta.json
    ├── val_combination_meta.json
    ├── test_combination_meta.json
    └── label_map.csv

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📦 Data Description

🔹 Individual Asset Files

• PointCloud_Asset/
  Contains raw point clouds of individual clothing or body parts in .ply format.

• Video_Asset/
  Rendered 3D videos of individual assets showing different rotations or views.

• Label_Asset/
  Label information (e.g., category, class ID) for each individual asset.

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🔹 Combined Assets (Mannequin Representations)

• PointCloud_Combine/
  Combined point clouds representing mannequins wearing multiple assets. Split into train, val, and test sets.

• Video_Combine/
  Rendered 3D videos of mannequins with asset combinations. Also split into train, val, and test.

• Label_Combine/
  Label files corresponding to the combined point clouds and videos.

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🗂️ Metadata Files (meta/)

Each mata contains this detailed information:

• label_str: class name

• gender, pose, type

• wnlemmas: fine-grained semantic tags

• asset_meta.json:
  Metadata for individual assets

• combination_meta.json:
  Metadata for all combinations

• train_combination_meta.json, val_combination_meta.json, test_combination_meta.json:
  Define which combinations belong to each data split.

• label_map.csv:
  Maps label for the first data acquisition fullid and the second acquisition label fullid.

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📃 License

CC-BY 4.0

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📚 Citation

@misc{3DFDReal,
  title={3DFDReal: 3D Fashion Data from the Real World},
  author={Jiyoun Lim, Jungwoo Son, Alex Lee, Sun-Joong Kim, Nam Kyung Lee, Won-Joo Park},,
  year={2025},
  howpublished={\url{https://huggingface.co/datasets/kusses/3DFDReal}},
}

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💬 Contact

For questions, please reach out via [[email protected]] or use the Discussions tab on Hugging Face.