| --- |
| title: MeshFlow Demo |
| emoji: 🎨 |
| colorFrom: indigo |
| colorTo: blue |
| sdk: gradio |
| sdk_version: 6.6.0 |
| app_file: gradio_app.py |
| python_version: "3.10" |
| hardware: zero-gpu |
| pinned: false |
| license: other |
| license_name: fair-noncommercial-research-license |
| license_link: LICENSE |
| short_description: Generate artist-like meshes from point clouds with MeshFlow |
| models: |
| - facebook/meshflow |
| --- |
| # MeshFlow Demo |
|
|
| Interactive demo for **MeshFlow**, an efficient artistic mesh generation model from Meta AI and HKUST (CVPR 2026 Highlight). |
|
|
| Upload a point cloud or mesh, optionally add a reference image, and the model returns a new artist-like mesh in about one second — preview it in the browser and download as GLB. |
|
|
| - **Project page:** https://mesh-flow.github.io/ |
| - **Source code:** https://github.com/facebookresearch/meshflow |
| - **Model card:** https://huggingface.co/facebook/meshflow |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{li2026meshflow, |
| title={MeshFlow: Efficient Artistic Mesh Generation via MeshVAE and Flow-based Diffusion Transformer}, |
| author={Li, Weiyu and Toisoul, Antoine and Monnier, Tom and Shapovalov, Roman and Ranjan, Rakesh and Tan, Ping and Vedaldi, Andrea}, |
| booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, |
| year={2026}, |
| note={Highlight} |
| } |
| ``` |
|
|
| ## License |
|
|
| This demo is released under the FAIR Noncommercial Research License v1. See [LICENSE](LICENSE). |
|
|
