Code for our NeurIPS 2023 paper "LART: Neural Correspondence Learning with Latent Regularization Transformer for 3D Motion Transfer"

This is the PyTorch implementation of our NeurIPS 2023 paper LART: Neural Correspondence Learning with Latent Regularization Transformer for 3D Motion Transfer. Haoyu Chen, Hao Tang, Radu Timofte, Luc Van Gool, Guoying Zhao.

If you use our code or paper, please consider citing:

@inproceedings{chen2023LART,
  title={LART: Neural Correspondence Learning with Latent Regularization Transformer for 3D Motion Transfer},
  author={Chen, Haoyu and Tang, Hao and Timofte, Radu and Van Gool, Luc and Zhao, Guoying},
  booktitle={NeurIPS},
  year={2023}
}

We are still organizing the code; please bear with us for a while.

Part of our code is based on

3D transfer: NPT，

and inspired by:

3D transfer with Correspondence Learning: 3DCorNet，

Transformer framework: (https://github.com/lucidrains/vit-pytorch)

Many thanks!

MIT-2.0 License