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CMU_PanopticDataset_2.0

Official code repository for the paper:
American society of biomechanics early career achievement award 2020: Toward Portable and Modular Biomechanics Labs: How Video and IMU Fusion Will Change Gait Analysis
Eni Halilj*, Soyong Shin, Eric Rapp, Donglai Xiang
Journal of Biomechanics, 2021

figure [algorithm]: Algorithm Overview
Video1: Reconstructed SMPL Model

Installation

All programming package is only tested on Ubuntu 20.04 and CUDA 11.2 with python 3.8. In this installation section, we assume you are using the proper system.

Step 1. Clone the repository

git clone https://github.com/CMU-MBL/CMU_PanopticDataset_2.0.git <PATH>

Step 2. Install dependencies

We recommend to use Anaconda virtual environment with python3 for running demo of this repository. All dependencies can be installed by following commands:

conda create -n Panoptic python=3
conda activate Panoptic
pip install -r requirements.txt
conda install -c conda-forge ffmpeg

Step 3. Download Dataset

We have published CMU_Panoptic_Dataset_2.0 at SimTK-project-page. Following the guideline of SimTK data share, you can download the dataset in your local machine. Once you download all data and extract the *.tar.gz files, you need to make a soft link of the dataset to the repository.

mkdir data
ln -s <DATASET PATH> ./data/

Step 4. Download relevant files for SMPL model

In order to run optimization fitting on 3D body model, you need to download few files as below:

  1. SMPL model
    SMPL_MALE.pkl, SMPL_FEMALE.pkl, SMPL_NEUTRAL.pkl
    Can be found on the SMPL-Official-Homepage.

  2. SMPL regression matrix
    SMPLCOCORegressor.npy
    Can be downloaded by Here

  3. SMPL mean parameters and Gaussian pose prior
    smpl_mean_params.npz gmm08.pkl
    Visit SPIN-Official-Repository and get those files by fetching data.

Then place those files as following structure:

$ Directory tree
.
├── data\
    ├── CMU_Panoptic_Dataset_2.0\
    │    ├── ...
    │
    └── body_models\
        ├── smpl\
        │   ├── SMPL_MALE.pkl
        │   ├── SMPL_FEMALE.pkl
        │   └── SMPL_NEUTRAL.pkl
        ├── SMPLCOCORegressor.npy
        ├── smpl_mean_params.npz
        └── gmm08.pkl

Run demo fitting

Demo fitting can be simply computed by running run_fitting.py. We provide basic options for fitting algorithm in utils/fitting_options.py. Following the namespace of parsing arguments, you can run your own fitting.

One example is:
python run_fitting.py --subject 'S01' --activity 'walking_direction1' --viz-results

This will run optimization on Subject S01 with walking_direction1 activity and save video of SMPL mesh results. The video below is the sample results of the fitting: Video1: Reconstructed SMPL Model

Citation

If you find this code or our data useful for your research, please cite the following paper:

@article{HALILAJ2021110650,
  title = {American society of biomechanics early career achievement award 2020: Toward portable and modular biomechanics labs: How video and IMU fusion will change gait analysis},
  journal = {Journal of Biomechanics},
  volume = {129},
  pages = {110650},
  year = {2021},
  issn = {0021-9290},
  doi = {https://doi.org/10.1016/j.jbiomech.2021.110650},
  url = {https://www.sciencedirect.com/science/article/pii/S002192902100419X},
  author = {Eni Halilaj and Soyong Shin and Eric Rapp and Donglai Xiang},
  }