🐎 SNK 🐎

Pytorch code for "SNK: Shape Non-rigid Kinematics" - NeurIPS 2023

Zero-shot non-rigid correspondence

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👷 Installation

This implementation requires Python >= 3.7. Install dependencies using pip:

pip install -r requirements.txt

📖 Usage

The full SNK pipeline will be available soon. Stay tuned!

Meanwhile, we release the code for the prism decoder (Section 4.2 in the paper). The prism decoder is a neural network that takes as input a source shape, and a latent code, and deform the source shape to a target shape that corresponds to the latent code. The code is available in the prism_decoder folder.

📈 Results

If you wish to report our result, we have summarized them below. Our method is referred to as GeomFmaps - clover. X on Y indicates that the method was trained on dataset X and tested on dataset Y.

• Near Isometric Shape Matching: We provide results on the FAUST (F), Scape (S), and SHREC (SH) datasets. We used the remeshed versions. We report the mean geodesic error, following the protocol used in all deep functional map papers. Our method is Zero-Shot, i.e. does not require any training on the train set, and is applied directly on the test set.

  Method	F	S	SH
  SNK	1.8	4.7	5.8

• Non-Isometric Shape Matching: We provide results on the SMAL dataset. We report the mean geodesic error, following the same protocol as in all the deep functional maps papers. Our method is Zero-Shot, i.e. does not require any training on the train set, and is applied directly on the test set.

  Method	SMAL
  SNK	9.1

🎓 Citation

If you find this work useful in your research, please consider citing:

@inproceedings{attaiki2023snk,
    title={Shape Non-rigid Kinematics (SNK): A Zero-Shot Method for Non-Rigid Shape Matching via Unsupervised Functional Map Regularized Reconstruction},
    author={Souhaib Attaiki and Maks Ovsjanikov},
    booktitle={Advances in Neural Information Processing Systems},
    year={2023}
}