---
license: mit
task_categories:
  - other
tags:
  - 3d
  - mesh
  - mesh-generation
  - flow-matching
  - shapenet
  - objaverse
pretty_name: MeshFlow
---

# MeshFlow

**Mesh Generation with Equivariant Flow Matching**

This repository hosts the pretrained model checkpoints and the processed mesh
dataset for **MeshFlow**.

🌐 **Project page:** https://qiisun.github.io/MeshFlow/
💻 **Code (GitHub):** https://github.com/qiisun/MeshFlow
📄 **Paper:** https://arxiv.org/abs/2606.12345
🤗 **Demo:** https://huggingface.co/spaces/qiisun/MeshFlow

---

## Repository Structure

```
.
├── v1/         # Pretrained model checkpoints
└── obj_data/   # Processed mesh dataset (.obj)
```

### `v1/` — Pretrained Checkpoints

Pretrained MeshFlow model weights. Download and use them together with the
inference code in the [GitHub repository](https://github.com/qiisun/MeshFlow).

### `obj_data/` — Dataset

The processed mesh dataset used to train and evaluate MeshFlow, stored as `.obj`
meshes.

---

## Usage

You can download the files with `huggingface_hub`:

```python
from huggingface_hub import snapshot_download

# Download everything
snapshot_download(repo_id="qsun2001/meshflow", repo_type="dataset")

# Download only the pretrained checkpoints
snapshot_download(
    repo_id="qsun2001/meshflow",
    repo_type="dataset",
    allow_patterns="v1/*",
)

# Download only the dataset
snapshot_download(
    repo_id="qsun2001/meshflow",
    repo_type="dataset",
    allow_patterns="obj_data/*",
)
```

Or with the CLI:

```bash
huggingface-cli download qsun2001/meshflow --repo-type dataset --local-dir ./meshflow
```

For training and inference instructions, please refer to the
[GitHub repository](https://github.com/qiisun/MeshFlow).

---

## Citation

If you find MeshFlow useful in your research, please consider citing:

```bibtex
@inproceedings{sun2026meshflow,
  title     = {MeshFlow: Mesh Generation with Equivariant Flow Matching},
  author    = {Sun, Qi and Nakayama, Kiyohiro and Yan, Jing Nathan and
               Huang, Qixing and Rush, Alexander and Guibas, Leonidas and
               Wetzstein, Gordon and Liao, Jing and Yang, Guandao},
  booktitle = {SIGGRAPH},
  year      = {2026}
}
```