| --- |
| license: other |
| license_name: snap-non-commercial-license |
| license_link: LICENSE |
| datasets: |
| - allenai/objaverse |
| language: |
| - en |
| pipeline_tag: image-to-3d |
| --- |
| ## Model Details |
|
|
| GTR is a large 3D reconstruction model that takes multi-view images as input and enables the generation of high-quality meshes with faithful texture reconstruction within seconds. |
|
|
| ## Model Description |
|
|
| <!-- Provide a longer summary of what this model is. --> |
|
|
| - **Developed by:** [Snap Research](https://github.com/snap-research) |
| - **License:** [snap-non-commercial-license](https://huggingface.co/snap-research/gtr/blob/main/LICENSE) |
|
|
| ## Model Sources |
|
|
| <!-- Provide the basic links for the model. --> |
|
|
| - **Repository:** [snap_gtr](https://github.com/snap-research/snap_gtr) |
| - **Paper:** [arxiv](https://arxiv.org/abs/2406.05649) |
| - **Web:** [project](https://snap-research.github.io/GTR/) |
|
|
| ## How to Get Started with the Model |
|
|
| ### Installation |
|
|
| We recommend using `Python>=3.10`, `PyTorch==2.7.0`, and `CUDA>=12.4`. |
| ```bash |
| conda create --name gtr python=3.10 |
| conda activate gtr |
| pip install -U pip |
| |
| pip install torch==2.7.0 torchvision==0.22.0 torchmetrics==1.2.1 --index-url https://download.pytorch.org/whl/cu124 |
| pip install -U xformers --index-url https://download.pytorch.org/whl/cu124 |
| |
| pip install -r requirements.txt |
| ``` |
|
|
| ### How to Use |
|
|
| Please follow instructions [here](https://github.com/snap-research/snap_gtr/tree/main?tab=readme-ov-file#how-to-use). |
|
|
| ## Demo |
|
|
|  |
|
|
| ## Citation |
|
|
| <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
|
|
| **BibTeX:** |
|
|
| ```bibtex |
| @article{zhuang2024gtr, |
| title={Gtr: Improving large 3d reconstruction models through geometry and texture refinement}, |
| author={Zhuang, Peiye and Han, Songfang and Wang, Chaoyang and Siarohin, Aliaksandr and Zou, Jiaxu and Vasilkovsky, Michael and Shakhrai, Vladislav and Korolev, Sergey and Tulyakov, Sergey and Lee, Hsin-Ying}, |
| journal={arXiv preprint arXiv:2406.05649}, |
| year={2024} |
| } |
| ``` |
|
|