Datasets:
Tasks:
Text-to-Video
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
1K - 10K
ArXiv:
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File size: 3,001 Bytes
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pretty_name: 3D-CustomBench
language:
- en
task_categories:
- text-to-video
tags:
- multi-view
- subject-customization
- video-generation
- 3d-aware
license: cc-by-4.0
---
# 3D-CustomBench
3D-CustomBench is the multi-view subject benchmark introduced in **3DreamBooth: High-Fidelity 3D Subject-Driven Video Generation Model**. It contains 30 subjects with ordered multi-view captures, background-normalized reference images, and evaluation prompts for customized video generation.
## Dataset summary
| Item | Count |
|---|---:|
| Subjects | 30 |
| Multi-view images | 897 |
| Reference images | 122 |
Each subject provides full 360-degree visual coverage for evaluating subject fidelity and 3D geometric consistency.
## Dataset structure
```text
3D-CustomBench/
├── README.md
├── manifest.json
└── subjects/
└── graduation_bear/
├── images/
│ ├── 001.jpeg
│ └── ...
├── references/
│ ├── 001.png
│ └── ...
├── metadata.json
└── prompt.txt
```
- `images/`: ordered multi-view captures used for subject customization and evaluation.
- `references/`: background-normalized conditioning images used by 3Dapter and Joint.
- `prompt.txt`: subject-specific evaluation prompt.
- `metadata.json`: stable public ID, legacy ID, prompt, and file counts.
- `manifest.json`: index and metadata for all subjects.
Public subject IDs use descriptive `snake_case`. The `legacy_id` field is retained only to reproduce internal experiments.
## Download
```bash
hf download lanikoworld/3D-CustomBench \
--repo-type dataset \
--local-dir ./datasets/3d-custombench
```
From the 3DreamBooth repository:
```bash
python scripts/data/download_custombench.py
```
## 3DreamBooth example
```bash
python scripts/run.py configs/examples/graduation_bear/train_joint.yaml
python scripts/run.py configs/examples/graduation_bear/validate_joint.yaml
```
## License
3D-CustomBench is released under the [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/) (`CC BY 4.0`). You may share and adapt the dataset for any purpose with appropriate attribution. This license covers only rights held by the dataset authors; third-party rights such as trademarks are not granted.
## Citation
If you use 3D-CustomBench, please cite the 3DreamBooth paper:
```bibtex
@misc{ko20263dreambooth,
title = {3DreamBooth: High-Fidelity 3D Subject-Driven Video Generation Model},
author = {Hyun-kyu Ko and Jihyeon Park and Younghyun Kim and Dongheok Park and Eunbyung Park},
year = {2026},
eprint = {2603.18524},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2603.18524}
}
```
- [Paper](https://arxiv.org/abs/2603.18524)
- [Project page](https://ko-lani.github.io/3DreamBooth/)
- [Code](https://github.com/Ko-Lani/3DreamBooth)
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