https://github.com/MotionMLP/MotionMixer

This is the official implementation of the paper "MotionMixer: MLP-based 3D Human Body Pose Forecasting" (IJCAI 2022, oral-presentation).
https://github.com/MotionMLP/MotionMixer

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This is the official implementation of the paper "MotionMixer: MLP-based 3D Human Body Pose Forecasting" (IJCAI 2022, oral-presentation).

• Host: GitHub
• URL: https://github.com/MotionMLP/MotionMixer
• Owner: MotionMLP
• Created: 2022-05-01T23:42:12.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-09-22T16:28:17.000Z (almost 2 years ago)
• Last Synced: 2024-04-05T10:32:59.429Z (6 months ago)
• Language: Python
• Homepage:
• Size: 693 KB
• Stars: 46
• Watchers: 3
• Forks: 6
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        



MotionMixer: MLP-based 3D Human Body Pose Forecasting 


Official PyTorch Implementation of the paper: MotionMixer: MLP-based 3D Human Body Pose Forecasting.

Arij Bouazizi, Adrian Holzbock, Ulrich Kressel, Klaus Dietmayer and Vasileios Belagiannis

 [[Proceedings](https://www.ijcai.org/proceedings/2022/0111.pdf)] [[Papers with Code](https://paperswithcode.com/paper/motionmixer-mlp-based-3d-human-body-pose)] [[Arxiv](https://arxiv.org/abs/2207.00499)]









## Installation

To setup the environment:

```sh

cd MotionMixer

conda create -n MotionMixer python=3.8.8

conda activate MotionMixer

pip install -r requirements.txt

```

## Data

Due to licensing it is not possible to provide any data. Please refer to [STSGCN](https://github.com/FraLuca/STSGCN) for the preparation of the dataset files.

## Training

To train the model on h36m or amass, you can use the following commands:

 ```

 python h36m/train_mixer_h36m.py --input_n 10 --output_n 25 --skip_rate 1 

 ```

 ```

 python amass/train_mixer_amass.py --input_n 10 --output_n 25 --skip_rate 5 

 ```

 

 ## Evaluation

To test the pretrained models, you can use the following commands:

 ```

 python h36m/test_mixer_h36m.py --input_n 10 --output_n 25 --skip_rate 1 

 ```

 ```

 python amass/test_mixer_amass.py --input_n 10 --output_n 25 --skip_rate 5 

 ```

 

 ## Models

We release the pretrained models for academic purpose. You can download them from [Google Drive](https://drive.google.com/drive/folders/1SrZpoe__Q3YXdk_TrtcxeJzRQiKAWoT5). Unzip the .zip file in the ```/checkpoints``` directory.

## Citation

If you find this code useful for your research, please consider citing the following paper:

```latex

@inproceedings{ijcai2022p111,

  title     = {MotionMixer: MLP-based 3D Human Body Pose Forecasting},

  author    = {Bouazizi, Arij and Holzbock, Adrian and Kressel, Ulrich and Dietmayer, Klaus and Belagiannis, Vasileios},

  booktitle = {Proceedings of the Thirty-First International Joint Conference on

               Artificial Intelligence, {IJCAI-22}},

  publisher = {International Joint Conferences on Artificial Intelligence Organization},

  pages     = {791--798},

  year      = {2022},

  month     = {7},

}

```

 ## Acknowledgments

 

 Some of our code was adapted from [HisRepsItself](https://github.com/wei-mao-2019/HisRepItself) and [STSGCN](https://github.com/FraLuca/STSGCN). We thank the authors for making their code public.

 

## License




This work is licensed under Creative Commons Attribution-NonCommercial 4.0 International License.