README.md

---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
license: apache-2.0
pipeline_tag: text-to-3d
---

# FastMesh: Efficient Artistic Mesh Generation via Component Decoupling

This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration.

**FastMesh** is an efficient framework that generates artistic meshes by treating vertices and faces separately, significantly reducing redundancy and achieving more than 8x faster speed on mesh generation compared to state-of-the-art approaches, while producing higher mesh quality.

- 📚 Paper: [FastMesh: Efficient Artistic Mesh Generation via Component Decoupling](https://huggingface.co/papers/2508.19188)
- 🏠 Project Page: [https://jhkim0759.github.io/projects/FastMesh/](https://jhkim0759.github.io/projects/FastMesh/)
- 💻 Code: [https://github.com/jhkim0759/FastMesh](https://github.com/jhkim0759/FastMesh)

## 💡 Quick Start
Generate meshes from sampled point cloud with V1K variant:
```bash
python inference.py --mesh_path assets --variant V1K --batch_size 3
```
Generate meshes from sampled point cloud with V4K variant:
```bash
python inference.py --mesh_path assets --variant V4K --batch_size 1
```

## 📚 Citation
If you find our work helpful, please consider citing:
```bibtex
@misc{kim2025fastmesh,
      title={FastMesh: Efficient Artistic Mesh Generation via Component Decoupling}, 
      author={Jeonghwan Kim and Yushi Lan and Armando Fortes and Yongwei Chen and Xingang Pan},
      year={2025},
      eprint={2508.19188},
      archivePrefix={arXiv},
      url={https://arxiv.org/abs/2508.19188}, 
}
```