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license: cc-by-4.0 |
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pipeline_tag: robotics |
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--- |
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<h1 align="center">Particulate: Feed-Forward 3D Object Articulation</h1> |
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<p align="center"> |
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<a href="https://arxiv.org/abs/2512.11798"><img src="https://img.shields.io/badge/arXiv-2512.11798-b31b1b" alt="arXiv"></a> |
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<a href="https://ruiningli.com/particulate"><img src="https://img.shields.io/badge/Project_Page-green" alt="Project Page"></a> |
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<a href='https://github.com/ruiningli/particulate'><img src='https://img.shields.io/badge/GitHub-Code-black.svg?logo=github' alt='GitHub Code'></a> |
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<a href='https://huggingface.co/spaces/rayli/particulate'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Demo-blue'></a> |
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</p> |
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This model's weights are licensed under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). The accompanying code is licensed under [Apache-2.0](https://github.com/ruiningli/particulate/blob/main/LICENSE). |
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## 🌟 Overview |
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Particulate is a feed-forward approach that, given a single static 3D mesh of an everyday object, directly infers **all** attributes of the underlying articulated structure, including its 3D parts, kinematic structure, and motion constraints. |
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### Key Features |
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- **Ultra-fast Inference**: Our model recovers a fully articulated 3D object with a single forward pass in ~10 seconds. |
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- **SOTA Performance**: Our model significantly outperforms prior methods on the task of 3D articulation estimation. |
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- **GenAI Compatible**: Our model can also accurately infer the articulated structure of AI-generated 3D assets, enabling full-fledged generation of articulated assets from images or texts when combined with an off-the-shelf 3D generator. |
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## 💻 Code |
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The official code repository for Particulate can be found on GitHub: [https://github.com/ruiningli/particulate](https://github.com/ruiningli/particulate) |
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## 🔧 Installation |
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Our implementation is tested on pytorch==2.4.0 with cuda 12.4 on Ubuntu 22.04. |
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``` |
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conda create -n particulate python=3.10 |
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conda activate particulate |
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pip install -r requirements.txt |
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``` |
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## 🚀 Sample Usage |
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To use our model to predict the articulated structure of a custom 3D model (alternatively, you can try our [demo](https://huggingface.co/spaces/rayli/particulate) on HuggingFace without local setup): |
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```bash |
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python infer.py --input_mesh ./hunyuan3d-examples/foldingchair.glb |
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``` |
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The script will automatically download the pre-trained checkpoint from Huggingface. |
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### Extra Arguments for Inference |
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- `up_dir`: The up direction of the input mesh. Our model is trained on 3D models with up direction +Z. To achieve optimal result, it is important to make sure the input mesh follow the same convention. The script will automatically rotate the input model to be +Z up with this argument. You can use the visualization in the [demo](https://huggingface.co/spaces/rayli/particulate) to determine the up direction. |
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- `num_points`: The number of points to be sampled as input to the network. Note that we uniformly sample 50% of points and sample the remaining 50% from *sharp* edges. Please make sure the number of uniform points is larger than the number of faces in the input mesh. |
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- `min_part_confidence`: Increasing this value will merge parts that have low confidence scores to other parts. Consider increasing this value if the prediction is over segmented. |
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- `no_strict`: By default, the prediction will be post-processed to ensure that each articulated part is a union of different connected components in the original mesh (i.e., no connected components are split across parts). If the input mesh does **not** have clean connected components, please specify `--no_strict`. |
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## 📝 Citation |
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```bibtex |
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@article{li2025particulate, |
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title = {Particulate: Feed-Forward 3D Object Articulation}, |
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author = {Ruining Li and Yuxin Yao and Chuanxia Zheng and Christian Rupprecht and Joan Lasenby and Shangzhe Wu and Andrea Vedaldi}, |
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journal = {arXiv preprint arXiv:2512.11798}, |
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year = {2025} |
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} |
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``` |
