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Pose_to_SMPL

Fitting SMPL Parameters by 3D-pose Key-points

The repository provides a tool to fit SMPL parameters from 3D-pose datasets that contain key-points of human body.

The SMPL human body layer for Pytorch is from the smplpytorch repository.

Setup

1. The smplpytorch package

  • Run without installing: You will need to install the dependencies listed in environment.yml:

    • conda env update -f environment.yml in an existing environment, or
    • conda env create -f environment.yml, for a new smplpytorch environment

  • Install: To import SMPL_Layer in another project with from smplpytorch.pytorch.smpl_layer import SMPL_Layer do one of the following.

    • Option 1: This should automatically install the dependencies. 
      git clone https://github.com/gulvarol/smplpytorch.git
cd smplpytorch
pip install .
    • Option 2: You can install smplpytorch from PyPI. Additionally, you might need to install chumpy. 
      pip install smplpytorch

2. Download SMPL pickle files

  • Download the models from the SMPL website by choosing "SMPL for Python users". Note that you need to comply with the SMPL model license.
  • Extract and copy the models folder into the smplpytorch/native/ folder (or set the model_root parameter accordingly).

3. Download Dataset

Fitting

1. Executing Code

You can start the fitting procedure by the following code and the configuration file in fit/configs corresponding to the dataset_name will be loaded (the dataset_path can also be set in the configuration file):

python fit/tools/main.py --dataset_name [DATASET NAME] --dataset_path [DATASET PATH]

2. Output

  • Direction: The output SMPL parameters will be stored in fit/output

  • Format: The output are .pkl files, and the data format is:

    {
	"label": [The label of action],
	"pose_params": pose parameters of SMPL (shape = [frame_num, 72]),
	"shape_params": pose parameters of SMPL (shape = [frame_num, 10]),
	"Jtr": key-point coordinates of SMPL model (shape = [frame_num, 24, 3])
}

Citation

This repo is part of the Pangea project, if you find it useful for your research, please consider citing:

@article{li2023isolated,
  title={From isolated islands to pangea: Unifying semantic space for human action understanding},
  author={Li, Yong-Lu and Wu, Xiaoqian and Liu, Xinpeng and Dou, Yiming and Ji, Yikun and Zhang, Junyi and Li, Yixing and Tan, Jingru and Lu, Xudong and Lu, Cewu},
  journal={arXiv preprint arXiv:2304.00553},
  year={2023}
}