Improve model card for FastMesh

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by nielsr HF Staff - opened
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  1. README.md +34 -4
README.md CHANGED
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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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- 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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- - Code: [More Information Needed]
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- - Paper: [More Information Needed]
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- - Docs: [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+
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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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+ - ๐Ÿ“š Paper: [FastMesh: Efficient Artistic Mesh Generation via Component Decoupling](https://huggingface.co/papers/2508.19188)
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+ - ๐Ÿ  Project Page: [https://jhkim0759.github.io/projects/FastMesh/](https://jhkim0759.github.io/projects/FastMesh/)
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+ - ๐Ÿ’ป Code: [https://github.com/jhkim0759/FastMesh](https://github.com/jhkim0759/FastMesh)
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+
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+ ## ๐Ÿ’ก 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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+
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+ ## ๐Ÿ“š 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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+ ```