Exploring Versatile Prior for Human Motion via Motion Frequency Guidance (3DV2021)

Exploring Versatile Prior for Human Motion via Motion Frequency Guidance

[Video Demo] [Paper]

Installation

Requirements

• Python 3.6
• PyTorch 1.1.0

Please clone this repository and run the following command:

pip install -r requirements.txt

Then, download some checkpoints and human body models, and place them in the human_motion_prior/models.

Specifically,

• Download the pretrained VPoser v1.0 model (2.5 MB) here.
• Download the SMPLH model here.
• Download the SMPL model here for MALE and FEMALE and here for NEUTRAL.
• Download the regressor weights files 1) J_regressor_h36m.npy and 2) J_regressor_extra.npy here.

The models tree is like:

human_motion_prior/models
├── smpl
│   ├── SMPL_FEMALE.pkl
│   └── SMPL_MALE.pkl
│   └── SMPL_NEUTRAL.pkl
│   └── J_regressor_h36m.npy
│   └── J_regressor_extra.npy
├── smplh
│   ├── SMPLH_FEMALE_AMASS.npz
│   └── SMPLH_MALE_AMASS.npz
|   └── SMPLH_MEUTRAL_AMASS.npz
├── pre_trained
    └── vposer_v1_0

Data Pre-process

Follow the instruction here.

Training

cd human_motion_prior/train
sh run_script.sh 4

We train our human motion prior on 4 GTX 1080Ti gpus with batch size 15 per gpu.

You can modify the human_motion_prior/train/motion_prior_defaults.ini for different training setting.

Inference

You can use the motion prior trained on AMASS to evaluate the VAE reconstruction loss on unseen 3DPW dataset as follows:

cd human_motion_prior/test
export PYTHONPATH=../../
python test_3dpw.py

Citation

@inproceedings{human_motion_prior,
  title = {Exploring Versatile Prior for Human Motion via Motion Frequency Guidance},
  author = {Jiachen Xu, Min Wang, Jingyu Gong, Wentao Liu, Chen Qian, Yuan Xie, Lizhuang Ma},
  booktitle = {2021 international conference on 3D vision (3DV)},
  year = {2021}
}

Acknowledgments

We thank the authors of VPoser for their released code.