---
license: cc-by-4.0
language:
- en
tags:
- 3d-vision
- fashion
- point-cloud
- virtual-try-on
- metaverse
- segmentation
- reconstruction
pretty_name: 3DFDReal
size_categories:
- 1K<n<10K
---

<div align="center">

# 🧵 3DFDReal: Real-World 3D Fashion Dataset

### *Empowering Virtual Try-On Applications with High-Quality 3D Fashion Data*

[![License: CC BY 4.0](https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)
[![Dataset Size](https://img.shields.io/badge/Point%20Clouds-1000%2B-blue)](https://huggingface.co/datasets/kusses/3DFDReal)
[![Resolution](https://img.shields.io/badge/Video-4K%20%40%2060fps-green)](https://huggingface.co/datasets/kusses/3DFDReal)

<!-- **Electronics and Telecommunications Research Institute (ETRI)**  
*Media Intellectualization Research Section* -->

**[Research Institution]**  
*[Research Department]*

</div>

---

## 🌟 Highlights

<div align="center">

![Teaser](figures/teaser.png)

</div>

<table>
<tr>
<td width="25%" align="center">

### 📊 1,000+
**3D Point Clouds**  
High-quality captures

</td>
<td width="25%" align="center">

### 🎥 4K@60fps
**Multi-View Videos**  
Professional recording

</td>
<td width="25%" align="center">

### 🏷️ Rich Metadata
**Detailed Annotations**  
Semantic labels & attributes

</td>
<td width="25%" align="center">

### 🎮 Metaverse Ready
**ZEPETO Compatible**  
Direct deployment support

</td>
</tr>
</table>

---

## 🎯 What is 3DFDReal?

**3DFDReal** is a groundbreaking real-world fashion dataset designed for cutting-edge 3D vision research. Bridging the gap between high-quality 3D fashion modeling and practical virtual environment deployment, our dataset provides researchers and developers with:

- ✨ **Individual fashion items** and **complete outfit combinations**
- 🎭 **Gender-balanced** mannequin representations
- 🔧 **Rigging-ready** T-pose and upright pose variations
- 📝 **Comprehensive metadata** including semantic attributes and structured segmentations

Perfect for applications in:
- 🛍️ Virtual Try-On Systems
- 👤 Avatar Modeling & Customization
- 🎨 Digital Fashion Design
- 🌐 Metaverse Asset Creation
- 🤖 Pose-Aware 3D Understanding

---

## 🔬 Data Collection Pipeline

![Data Collection Pipleline](figures/datacollection.png)

### Pipeline Stages:

1. **📦 Asset Selection**  
   Curated selection of fashion items with detailed tagging (individual items & complete sets)

2. **📹 Recording Setup**  
   Professional capture using iPhone 13 Pro with controlled lighting and multi-angle coverage

3. **☁️ 3D Ground Truth Generation**  
   High-fidelity point cloud generation with manual segmentation using professional 3D tools

4. **🎮 Application & Validation**  
   Rigging and deployment testing in real metaverse environments (ZEPETO)

---

## 📈 Dataset Statistics

### 👔 Fashion Class Distribution

<div align="center">

![Fashion Class Distribution](figures/fashion_class_distribution.png)

*Distribution of fashion items across the dataset, with pants and sweatshirts being the most represented categories*

</div>

### 🎭 Combination Analysis

<div align="center">

![Combination Frequency](figures/Count appears in Combination.png)

*Sneakers and pants appear most frequently in mannequin outfit combinations*

</div>

### 📊 Dataset Composition

<div align="center">

![Combination Overview](figures/combination_overview_stats.png)

</div>

#### Key Insights:
- 📦 **Average items per outfit**: 4 distinct fashion pieces
- ⚖️ **Gender balance**: Equal representation across combinations
- 🕴️ **Pose distribution**: Upright poses (standard) + T-poses (rigging-optimized)

---

## 📂 Dataset Organization

```
3DFDReal/
│
├── 🎨 Assets (Individual Items)
│   ├── PointCloud_Asset/     # Raw .ply point clouds
│   ├── Video_Asset/          # 3D rotation videos
│   └── Label_Asset/          # Category & class labels
│
├── 👕 Combinations (Full Outfits)
│   ├── PointCloud_Combine/   # Mannequin point clouds
│   │   ├── train/
│   │   ├── val/
│   │   └── test/
│   ├── Video_Combine/        # Mannequin videos
│   └── Label_Combine/        # Combination labels
│
└── 📋 Metadata
    ├── asset_meta.json               # Individual item metadata
    ├── combination_meta.json         # All combinations
    ├── {train,val,test}_combination_meta.json
    └── label_map.csv                # Label mapping reference
```

### 🔑 Metadata Schema

Each metadata entry contains:
- `label_str`: Human-readable class name
- `gender`: Male/Female/Unisex
- `pose`: T-pose/Upright
- `type`: Asset/Combination
- `wnlemmas`: Fine-grained semantic tags

---

## 🏆 Benchmark Results

### 🎯 3D Object Segmentation

Using [**SAMPart3D**](https://yhyang-myron.github.io/SAMPart3D-website/) as baseline:

<div align="center">

| Metric | Score |
|--------|-------|
| **mIoU** | 0.9930 |
| **Average Precision** | Class-dependent |

![Segmentation Results](figures/seg_tuning.png)

</div>

### 🔨 3D Reconstruction

Baseline models evaluated:
- **Generation**: [DDPM-A](https://github.com/lucidrains/denoising-diffusion-pytorch) 
- **Completion**: [SVD-SVDFormer](https://github.com/czvvd/SVDFormer_PointSea)

<div align="center">

| Model | CD | DCD | F1-Score |
|-------|-----|-----|----------|
| **DDPM-A** | 0.628±0.887 | - | - |

![Reconstruction Example](figures/sampledPC.png)

</div>

---

## 🚀 Getting Started

### Quick Start

```python
# Load the dataset from Hugging Face
from datasets import load_dataset

dataset = load_dataset("kusses/3DFDReal")

# Access different splits
train_data = dataset['train']
val_data = dataset['validation']
test_data = dataset['test']
```

### Example Use Cases

- 🛍️ **Virtual Try-On Applications**
- 🌐 **Metaverse Asset Generation**
- 🤖 **Pose-Aware Segmentation Research**
- 👤 **Avatar Rigging & Deformation**
- 🎨 **Digital Fashion Design Tools**

---

## 📝 Citation

If you use 3DFDReal in your research, please cite:

```bibtex
@misc{3DFDReal2025,
  title={3DFDReal: Real-World 3D Fashion Dataset for Virtual Try-On Applications},
  author={[Research Team]},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/kusses/3DFDReal}},
  note={[Research Institution]}
}
```

---

## 🤝 Contributing

We welcome contributions to improve and expand 3DFDReal! Please feel free to:
- 🐛 Report issues or bugs
- 💡 Suggest new features or improvements
- 🔧 Submit pull requests
- 💬 Join discussions on our [Hugging Face page](https://huggingface.co/datasets/kusses/3DFDReal)

---

## 📬 Contact

<div align="center">

<!-- **Jiyoun Lim**  
Electronics and Telecommunications Research Institute (ETRI)  
📧 [kusses@etri.re.kr](mailto:kusses@etri.re.kr) -->

**[Research Team Representative]**  
[Research Institution]  
📧 [Contact email available upon request]

[![Hugging Face](https://img.shields.io/badge/🤗%20Hugging%20Face-Datasets-yellow)](https://huggingface.co/datasets/kusses/3DFDReal)
[![GitHub](https://img.shields.io/badge/GitHub-Discussion-black)](https://huggingface.co/datasets/kusses/3DFDReal/discussions)

</div>

---


<div align="center">

### 📄 License

This dataset is released under the **Creative Commons Attribution 4.0 International License (CC BY 4.0)**

<a href="https://creativecommons.org/licenses/by/4.0/">
<img src="https://licensebuttons.net/l/by/4.0/88x31.png" alt="CC BY 4.0" />
</a>

</div>

---

<div align="center">
<!-- <sub>Made with ❤️ by Media Intellectualization Research Team, ETRI</sub> -->
<sub>Made with ❤️ by [Research Team]</sub>
</div>