Source code of our BMVC 2021 paper: AniFormer: Data-driven 3D Animation with Transformer

Last update: Nov 02, 2022

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Overview

AniFormer

This is the PyTorch implementation of our BMVC 2021 paper AniFormer: Data-driven 3D Animation with Transformer. Haoyu Chen, Hao Tang, Nicu Sebe, Guoying Zhao.

Citation

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

@inproceedings{chen2021AniFormer,
  title={AniFormer: Data-driven 3D Animation withTransformer},
  author={Chen, Haoyu and Tang, Hao and Sebe, Nicu and Zhao, Guoying},
  booktitle={BMVC},
  year={2021}
}

Dependencies

Requirements:

python3.6
numpy
pytorch==1.1.0 and above
trimesh

Dataset preparation

Please download DFAUST dataset from DFAUST link for training the model.

Generate the driving sequence based on our script:

TBD

Training

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

python train.py

Acknowledgement

Part of our code is based on

3D transfer: NPT，

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

Many thanks!

License

MIT-2.0 License

Owner

A researcher in Oulu, Finland

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