🧵 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.

---

🔍 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.

---

🎥 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.

---

📊 Dataset Statistics

📈 Class Distribution

Pants and sweatshirts are used more than other fashion items.

 Sneakers and Pants are the most frequent fashion items in Mannequin-wear combinations.

👚 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.

---

📁 Dataset Structure

dataset/
├── PointCloud_Asset/
├── Video_Asset/
├── Label_Asset/
├── PointCloud_Combine/
├── Video_Combine/
├── Label_Combine/
└── meta/
    ├── asset_meta.json
    ├── combination_meta.json
    ├── train_combination_meta.json
    ├── val_combination_meta.json
    ├── test_combination_meta.json
    └── label_map.csv

---

📦 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.

---

🔹 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.

---

🗂️ 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.

---

🧪 Benchmarks

3D object segmentation

The Baseline model using SAMPart3D demonstrates high segmentation quality (mIoU: 0.9930) but shows varying average precision (AP) across classes.

3D data reconstruction

The baseline models are DDPM-A diffusion-based probabilistic model for the generation task, and SVD-SVDFormer for the completion task. Performance is measured using Chamfer Distance (CD), Density-aware Chamfer Distance (DCD), and F1-Score (F1). For DDPM, sampled point clouds are shuffled without considering the sampling ratio 𝑛, and the performance of DDPM is measured with CD. DDPM shows 0.628±0.887 of the average CD.

---

💻 Use Cases

• Virtual try-on
• Metaverse asset creation
• Pose-aware segmentation
• Avatar rigging & deformation simulation

📃 License

CC-BY 4.0

---

📚 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}},
}

---

💬 Contact

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