pose2smpl

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

Download the datasets you want to fit

currently support:
- HumanAct12
- CMU Mocap
- UTD-MHAD
- Human3.6M
- NTU
- HAA4D
Set the DATASET.PATH in the corresponding configuration file to the location of 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])
}

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