MRT

This is an implementation of the NeurIPS'21 paper "Multi-Person 3D Motion Prediction with Multi-Range Transformers".

Please check our paper and the project webpage for more details.

We will also provide the code to fit our skeleton representation data to SMPL data.

Citation

If you find our code or paper useful, please consider citing:

@article{wang2021multi,
  title={Multi-Person 3D Motion Prediction with Multi-Range Transformers},
  author={Wang, Jiashun and Xu, Huazhe and Narasimhan, Medhini and Wang, Xiaolong},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}

Dependencies

Requirements:

• python3.6
• pytorch==1.1.0
• torch_dct
• AMCParser

Datasets

We provide the data preprocessing code of CMU-Mocap and MuPoTS-3D (others are coming soon). For CMU-Mocap, the dictionary tree is like

   mocap
   ├── amc_parser.py
   ├── mix_mocap.py
   ├── preprocess_mocap.py
   ├── vis.py
   └── all_asfamc
       └── subjects
           ├── 01
           ...

After dowloading the original data, please try

python ./mocap/preprocess_mocap.py
python ./mocap/mix_mocap.py

For MuPoTS-3D, the dictionary tree is like

   mupots3d
   ├── preprocess_mupots.py
   ├── vis.py
   └── data
       ├── TS1
       ...

After dowloading the original data, please try

python ./mocap/preprocess_mupots.py

Training

To train our model, please try

python train_mrt.py

Evaluation and visualization

We provide the evaluation and visualization code in test.py

Acknowledgement

This work was supported, in part, by grants from DARPA LwLL, NSF CCF-2112665 (TILOS), NSF 1730158 CI-New: Cognitive Hardware and Software Ecosystem Community Infrastructure (CHASE-CI), NSF ACI-1541349 CC*DNI Pacific Research Platform, and gifts from Qualcomm, TuSimple and Picsart. Part of our code is based on attention-is-all-you-need-pytorch and AMCParser. Many thanks!