| 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}, | |
| } | |
| ``` |