Attention Mechanism Exploits Temporal Contexts: Real-time 3D Human Pose Reconstruction (CVPR 2020 Oral)

More extensive evaluation andcode can be found at our lab website: (https://sites.google.com/a/udayton.edu/jshen1/cvpr2020)

     

PyTorch code of the paper "Attention Mechanism Exploits Temporal Contexts: Real-time 3D Human Pose Reconstruction". pdf

Bibtex

If you found this code useful, please cite the following paper:

@inproceedings{liu2020attention,
  title={Attention Mechanism Exploits Temporal Contexts: Real-Time 3D Human Pose Reconstruction},
  author={Liu, Ruixu and Shen, Ju and Wang, He and Chen, Chen and Cheung, Sen-ching and Asari, Vijayan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={5064--5073},
  year={2020}
}

Environment

The code is developed and tested on the following environment

• Python 3.6
• PyTorch 1.1 or higher
• CUDA 10

Dataset

The source code is for training/evaluating on the Human3.6M dataset. Our code is compatible with the dataset setup introduced by Martinez et al. and Pavllo et al.. Please refer to VideoPose3D to set up the Human3.6M dataset (./data directory).

Training new models

To train a model from scratch, run:

python run.py -da -tta

-da controls the data augments during training and -tta is the testing data augmentation.

For example, to train our 243-frame ground truth model or causal model in our paper, please run:

python run.py -k gt

or

python run.py -k cpn_ft_h36m_dbb --causal

It should require 24 hours to train on one TITAN RTX GPU.

Evaluating pre-trained models

We provide the pre-trained cpn model here and ground truth model here. To evaluate them, put them into the ./checkpoint directory and run:

For cpn model:

python run.py -tta --evaluate cpn.bin

For ground truth model:

python run.py -k gt -tta --evaluate gt.bin

Visualization and other functions

We keep our code consistent with VideoPose3D. Please refer to their project page for further information.