README.md

metadata

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.

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:

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:

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

For training and inference instructions, please refer to the GitHub repository.

---

Citation

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

@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}
}