Improve model card for FastMesh
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by
nielsr HF Staff - opened
README.md
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tags:
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- model_hub_mixin
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- pytorch_model_hub_mixin
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---
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tags:
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- model_hub_mixin
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- pytorch_model_hub_mixin
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license: apache-2.0
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pipeline_tag: text-to-3d
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---
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# FastMesh: Efficient Artistic Mesh Generation via Component Decoupling
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This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration.
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**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.
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- ๐ Paper: [FastMesh: Efficient Artistic Mesh Generation via Component Decoupling](https://huggingface.co/papers/2508.19188)
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- ๐ Project Page: [https://jhkim0759.github.io/projects/FastMesh/](https://jhkim0759.github.io/projects/FastMesh/)
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- ๐ป Code: [https://github.com/jhkim0759/FastMesh](https://github.com/jhkim0759/FastMesh)
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## ๐ก Quick Start
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Generate meshes from sampled point cloud with V1K variant:
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```bash
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python inference.py --mesh_path assets --variant V1K --batch_size 3
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```
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Generate meshes from sampled point cloud with V4K variant:
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```bash
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python inference.py --mesh_path assets --variant V4K --batch_size 1
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```
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## ๐ Citation
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If you find our work helpful, please consider citing:
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```bibtex
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@misc{kim2025fastmesh,
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title={FastMesh: Efficient Artistic Mesh Generation via Component Decoupling},
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author={Jeonghwan Kim and Yushi Lan and Armando Fortes and Yongwei Chen and Xingang Pan},
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year={2025},
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eprint={2508.19188},
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archivePrefix={arXiv},
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url={https://arxiv.org/abs/2508.19188},
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}
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```
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