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--- |
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license: mit |
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task_categories: |
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- image-to-3d |
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tags: |
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- 3d-physics |
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- material-properties |
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- gaussian-splatting |
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- clip-features |
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- 3d-assets |
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--- |
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# Pixie Dataset |
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This dataset contains data and pre-trained models for the paper [Pixie: Fast and Generalizable Supervised Learning of 3D Physics from Pixels](https://huggingface.co/papers/2508.17437). |
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- Project Page: https://pixie-3d.github.io/ |
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- Code: https://github.com/vlongle/pixie |
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## Contents |
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- `checkpoints_continuous_mse/`: Continuous material property prediction model checkpoints |
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- `checkpoints_discrete/`: Discrete material classification model checkpoints |
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- `real_scene_data/`: Real scene data for evaluation |
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- `real_scene_models/`: Trained models for real scenes |
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## Sample Usage |
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First, use the download script in the Pixie repository to automatically download this data and models: |
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```bash |
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python scripts/download_data.py |
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``` |
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Then, you can run the main pipeline with a synthetic Objaverse object, for example: |
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```python |
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python pipeline.py obj_id=f420ea9edb914e1b9b7adebbacecc7d8 material_mode=neural |
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``` |
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This command will: |
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1. Download the specified Objaverse asset. |
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2. Render it and train 3D representations (NeRF, Gaussian Splatting). |
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3. Generate a voxel feature grid. |
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4. Use the trained neural networks to predict the physics field. |
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5. Run the MPM physics solver using the predicted physics parameters. |
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For more detailed usage, including real-scene processing and training, refer to the [Github repository's usage section](https://github.com/vlongle/pixie#usage). |
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## Citation |
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If you find this work useful, please consider citing: |
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```bibtex |
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@article{le2025pixie, |
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title={Pixie: Fast and Generalizable Supervised Learning of 3D Physics from Pixels}, |
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author={Le, Long and Lucas, Ryan and Wang, Chen and Chen, Chuhao and Jayaraman, Dinesh and Eaton, Eric and Liu, Lingjie}, |
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journal={arXiv preprint arXiv:2508.17437}, |
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year={2025} |
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} |
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``` |
