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Genex-DB-World-Exploration

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Genex-DB-World-Exploration
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---
dataset_info:
  features:
  - name: video
    dtype: video
  splits:
  - name: view
    num_examples: 4
  - name: realistic
    num_examples: 3700
  - name: low_texture
    num_examples: 8400
  - name: anime
    num_examples: 900
  - name: real_world
    num_examples: 2400
configs:
- config_name: default
  data_files:
  - split: view
    path: view/*.mp4
  - split: realistic
    path: Realistic/*.mp4
  - split: low_texture
    path: Low-Texture/*.mp4
  - split: anime
    path: Anime/*.mp4
  - split: real_world
    path: Real-World/*.mp4
size_categories:
- 10K<n<100K
license: cc-by-4.0
---


# GenEx-DB-World-Exploration 🎬🌍

This is the video version of the GenEx-DB dataset. 

The dataset contains forward navigation path, captured by panoramic cameras.
Each path is 0.4m/frame, 50 frames in total.
Each example is a single `.mp4` video reconstructed from the original frame folders.

## 📂 Splits

| Split Name    | Description                          
|---------------|--------------------------------------
| `realistic`   | 📸 Unreal 5 City Sample renders            
| `low_texture` | 🏜️ Blender Low-texture synthetic scenes      
| `anime`       | 🌸 Unity Stylized/anime scenes             
| `real_world`  | 🎥 JHU campus handheld collected real-world clips    

## 🏗️ Structure

```
Genex-DB-Video/
├── low_texture/
│   ├── video001.mp4
│   └── …   
├── realistic/
│   └── …
├── anime/
│   └── …
└── real_world/
    └── …
```

Each file is named `<video_id>.mp4` and contains 50 (or 97 for `real_world`) frames at 10 FPS.

## 🚀 Usage

```python
from datasets import load_dataset

# Load the “anime” split
ds = load_dataset("videofolder", data_dir="genex-world/Genex-DB-World-Exploration", split="anime")

# Inspect one example
example = ds[0]
print(example["video"].shape)  # (num_frames, height, width, 3)
```


## ✨ BibTex

```
@misc{lu2025genexgeneratingexplorableworld,
      title={GenEx: Generating an Explorable World}, 
      author={Taiming Lu and Tianmin Shu and Junfei Xiao and Luoxin Ye and Jiahao Wang and Cheng Peng and Chen Wei and Daniel Khashabi and Rama Chellappa and Alan Yuille and Jieneng Chen},
      year={2025},
      eprint={2412.09624},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2412.09624}, 
}
```