History Repeats Itself: Human Motion Prediction via Motion Attention

The code is an iterative edition based on the paper:

Wei Mao, Miaomiao Liu, Mathieu Salzmann. History Repeats Itself: Human Motion Prediction via Motion Attention. In ECCV 20.

The framework has been addtionally coupled with keyseq mechanism.

Dependencies

• cuda 10.0
• Python 3.6
• Pytorch >1.0.0 (Tested on 1.1.0 and 1.3.0)

Get the data

Human3.6m in exponential map can be downloaded from here.

Directory structure:

H3.6m
|-- S1
|-- S5
|-- S6
|-- ...
`-- S11
(S5 for testing, the others for training)

AMASS from their official website..

Directory structure:

amass
|-- ACCAD
|-- BioMotionLab_NTroje
|-- CMU
|-- ...
`-- Transitions_mocap
(Unavailable)

3DPW from their official website.

Directory structure:

3dpw
|-- imageFiles
|   |-- courtyard_arguing_00
|   |-- courtyard_backpack_00
|   |-- ...
`-- sequenceFiles
    |-- test
    |-- train
    `-- validation

Put the all downloaded datasets in ./datasets directory.

Training

All the running args are defined in opt.py. We use following commands to train on different datasets and representations. To train,

python main_h36m_3d.py --kernel_size 10 --dct_n 20 --input_n 50 --output_n 10 --skip_rate 1 --batch_size 32 --test_batch_size 32 --in_features 66

python main_h36m_ang.py --kernel_size 10 --dct_n 20 --input_n 50 --output_n 10 --skip_rate 1 --batch_size 32 --test_batch_size 32 --in_features 48

python main_amass_3d.py --kernel_size 10 --dct_n 35 --input_n 50 --output_n 25 --skip_rate 5 --batch_size 128 --test_batch_size 128 --in_features 54 

Evaluation

To evaluate the pretrained model,

python main_h36m_3d_eval.py --is_eval --kernel_size 10 --dct_n 20 --input_n 50 --output_n 25 --skip_rate 1 --batch_size 32 --test_batch_size 32 --in_features 66 --ckpt ./checkpoint/pretrained/h36m_3d_in50_out10_dctn20/

python main_h36m_ang_eval.py --is_eval --kernel_size 10 --dct_n 20 --input_n 50 --output_n 25 --skip_rate 1 --batch_size 32 --test_batch_size 32 --in_features 48 --ckpt ./checkpoint/pretrained/h36m_ang_in50_out10_dctn20/

python main_amass_3d_eval.py --is_eval --kernel_size 10 --dct_n 35 --input_n 50 --output_n 25 --skip_rate 5 --batch_size 128 --test_batch_size 128 --in_features 54 --ckpt ./checkpoint/pretrained/amass_3d_in50_out25_dctn30/

Citing

If you use our code, please cite our work

@inproceedings{wei2020his,
  title={History Repeats Itself: Human Motion Prediction via Motion Attention},
  author={Wei, Mao and Miaomiao, Liu and Mathieu, Salzemann},
  booktitle={ECCV},
  year={2020}
}

Acknowledgments

The overall code framework (dataloading, training, testing etc.) is adapted from 3d-pose-baseline.

The predictor model code is adapted from LTD.

Some of our evaluation code and data process code was adapted/ported from Residual Sup. RNN by Julieta.

Licence

MIT

从命令行创建一个新的仓库

touch README.md
git init
git add README.md
git commit -m "first commit"
git remote add origin https://git.trustie.net/Emeslbct7/HumanMotionPrediction.git
git push -u origin master

从命令行推送已经创建的仓库

git remote add origin https://git.trustie.net/Emeslbct7/HumanMotionPrediction.git
git push -u origin master