aleflabo / BestPractices2Body

The official PyTorch implementation of the 5th IEEE/CVF CVPR Precognition Workshop paper Best Practices for 2-Body Pose Forecasting.

Best practices for 2-Body Pose Forecasting

The official PyTorch implementation of the 5th IEEE/CVF CVPR Precognition Workshop paper Best Practices for 2-Body Pose Forecasting.

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Content

.
├── assets
│   ├── poses-viz.png
│   └── teaser.png
├── dataset
│   └── pi
├── env.yaml
├── log
├── README.md
├── snapshot
│   └── model-iter-40000.pth
├── src
│   ├── datasets
│   │   ├── data_utils_pi3d.py
│   │   ├── pi3d_hier.py
│   │   ├── pi3d.py
│   │   └── vis_2p.py
│   ├── model.py
│   ├── test.py
│   ├── train.py
│   └── utils
│       ├── angle_to_joint.py
│       ├── config.py
│       ├── logger.py
│       ├── misc.py
│       ├── parser.py
│       ├── pyt_utils.py
│       ├── rigid_align.py
│       ├── util.py
│       └── visualize.py
└── viz

Setup

Environment

conda env create -f env.yaml
conda activate bp42b

Dataset

Request ExPI dataset here and place the pi folder under datasets/.

Training

PYTHONPATH=. python src/train.py

Test

PYTHONPATH=. python src/test.py

Visualization

PYTHONPATH=. python src/test.py --visualize

Results

Quantitative

On the common action split of ExPI dataset, we achieve the following results:

	5	10	15	25
MPJPE	40	87.1	130.1	201.3
AME	25	53	76	110

On the unseen action split of ExPI dataset, we achieve the following results:

	10	15	20
MPJPE	110.4	161.7	205.3
AME	65	93	114

Qualitative

Citation

@InProceedings{Rahman_2023_CVPR,
    author    = {Rahman, Muhammad Rameez Ur and Scofano, Luca and De Matteis, Edoardo and Flaborea, Alessandro and Sampieri, Alessio and Galasso, Fabio},
    title     = {Best Practices for 2-Body Pose Forecasting},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month     = {June},
    year      = {2023},
    pages     = {3614-3624}
}

Acknowledgements

We build upon siMLPe and take some code from MultiMotion.