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conda create -n particulate python=3.10
conda activate particulate
pip install -r requirements.txtpython infer.py --input_mesh ./hunyuan3d-examples/foldingchair.glbup_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 to determine the up direction.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.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.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.1@article{li2025particulate,
2 title = {Particulate: Feed-Forward 3D Object Articulation},
3 author = {Ruining Li and Yuxin Yao and Chuanxia Zheng and Christian Rupprecht and Joan Lasenby and Shangzhe Wu and Andrea Vedaldi},
4 journal = {arXiv preprint arXiv:2512.11798},
5 year = {2025}
6}