PyTorch implementation of our ICCV 2021 paper Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer.

Last update: Oct 26, 2022

Overview

Unsupervised_IEPGAN

This is the PyTorch implementation of our ICCV 2021 paper Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer.
Haoyu Chen, Hao Tang, Henglin Shi, Wei Peng, Nicu Sebe, Guoying Zhao.

Citation

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

@inproceedings{chen2021IEPGAN,
  title={Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer},
  author={Chen, Haoyu and Tang, Hao and Henglin, Shi and Peng, Wei and Sebe, Nicu and Zhao, Guoying},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year={2021}
}

Dependencies

Requirements:

python3.6
numpy
pytorch==1.1.0 and above
trimesh

Dataset preparation

Please download FAUST data from FAUST link, and DFAUST dataset from DFAUST link for training the model.

Usage

The usage of our code is easy, just run the code below.

bash run.sh

Your can change the hyperparameters according to your GPU settings and needs in the run.sh file:

python  train.py --batch 4 ./dataset_3d/FAUST/  --edge_loss_setting 'rec_rec' --experi_path 'full_adap_GIH_Lap_rec' --server 'local' --rec_epoch 1200 --geo_epoch 1600 --iter 2000 --geoloss 0.005 --n_crop 1 --ref_crop 2 --sampling_number 120 --limb_n 4 --limb_sampling 600  --sampling_pattern 'adaptive'

Acknowledgement

Part of our code is based on

3D transfer: NPT，

GAN framework: SWAP-Autoencoder (unofficial implementation) and

GIH implementation: LIMP. Many thanks!

License

MIT-2.0 License

PyTorch implementation of our ICCV 2021 paper Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer.

Related tags

Overview

Unsupervised_IEPGAN

Citation

Dependencies

Dataset preparation

Usage

Acknowledgement

License

Owner

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