---
language:
- en
license: mit
task_categories:
- image-to-3d
- text-to-3d
tags:
- 3D-Generation
- 3D-Edit
---

# Edit3D-Bench

[Paper](https://huggingface.co/papers/2508.19247) | [Project Page](https://huanngzh.github.io/VoxHammer-Page/) | [Code](https://github.com/Nelipot-Lee/VoxHammer)

**Edit3D-Bench** is a benchmark for 3D editing evaluation, introduced in the paper [VoxHammer: Training-Free Precise and Coherent 3D Editing in Native 3D Space](https://huggingface.co/papers/2508.19247).
This dataset comprises 100 high-quality 3D models, with 50 selected from Google Scanned Objects (GSO) and 50 from PartObjaverse-Tiny.
For each model, we provide 3 distinct editing prompts. Each prompt is accompanied by a complete set of annotated 3D assets, including
* original 3D asset with rendered images
* **3D mask** specifying the editing region with rendered images
* 2D mask of the edit region
* 2D edited image generated by FLUX.1 Fill

## Preview

Explore our dataset in [Project Page](https://huanngzh.github.io/VoxHammer-Page/dataset).

![image/png](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F6375d136dee28348a9c63cbf%2F33oW8RnFWOp2zs0vBHrQE.png)

## Which tasks will benefit from our dataset?

- [x] 3D Editing

## ⚙️ Getting Started

### Download the Dataset

To download the full dataset, you can use the following code. If you encounter any issues, please refer to the official Hugging Face documentation.

```Bash
# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install

# When prompted for a password, use an access token with write permissions.
# Generate one from your settings: https://huggingface.co/settings/tokens
git clone https://huggingface.co/datasets/huanngzh/Edit3D-Bench

# If you want to clone without large files - just their pointers
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/huanngzh/Edit3D-Bench

```

### Dataset Structure

```Bash
data/
├── metadata.json                   # Dataset metadata file (dataset, object_name, prompt)
├── GSO/                            # Google Scanned Objects dataset
│   ├── [object_name]/
│   │   ├── source_model/           # Original 3D model
│   │   │   ├── model.glb             # Original 3D model file (GLB format)
│   │   │   ├── render/               # Original model rendered images
│   │   │   ├── video_rgb.mp4         # Original model RGB video
│   │   │   ├── video_normal.mp4      # Original model normal video
│   │   │   └── video_mask.mp4        # Original model mask video
│   │   ├── prompt_1/               # Annotation for prompt 1
│   │   │   ├── 2d_edit.png           # 2D edited image
│   │   │   ├── 2d_mask.png           # 2D mask image for editing
│   │   │   ├── 2d_render.png         # 2D render image of original model
│   │   │   ├── 2d_visual.png         # 2D visualization image
│   │   │   ├── 3d_edit_region.glb    # 3D edit region model
│   │   │   └── render/               # Rendered images of 3D mask
│   │   ├── prompt_2/               # Annotation for prompt 2
│   │   └── prompt_3/               # Annotation for prompt 3
│   └── ...
└── PartObjaverse-Tiny/             # PartObjaverse-Tiny dataset
    ├── [object_id]/
    │   ├── source_model/           # Original 3D model
    │   ├── prompt_1/               # Annotation for prompt 1
    │   ├── prompt_2/               # Annotation for prompt 2
    │   └── prompt_3/               # Annotation for prompt 3
    └── ...
```

### Evaluation

Check details in [our github repo](https://github.com/Nelipot-Lee/VoxHammer/Edit3D-Bench).

## 🧷 Citation

```bibtex
@article{li2025voxhammer,
  title = {VoxHammer: Training-Free Precise and Coherent 3D Editing in Native 3D Space},
  author = {Li, Lin and Huang, Zehuan and Feng, Haoran and Zhuang, Gengxiong and Chen, Rui and Guo, Chunchao and Sheng, Lu},
  journal = {arXiv preprint arXiv:2508.19247},
  year = {2025},
  url = {https://huggingface.co/papers/2508.19247}
}
```