76 lines
2.7 KiB
Markdown
76 lines
2.7 KiB
Markdown
pose2smpl
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=======
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### Fitting SMPL Parameters by 3D-pose Key-points
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The repository provides a tool to fit **SMPL parameters** from **3D-pose** datasets that contain key-points of human body.
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The SMPL human body layer for Pytorch is from the [smplpytorch](https://github.com/gulvarol/smplpytorch) repository.
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<p align="center">
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<img src="assets/fit.gif" width="350"/>
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<img src="assets/gt.gif" width="350"/>
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</p>
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## Setup
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### 1. The `smplpytorch` package
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* **Run without installing:** You will need to install the dependencies listed in [environment.yml](environment.yml):
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* `conda env update -f environment.yml` in an existing environment, or
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* `conda env create -f environment.yml`, for a new `smplpytorch` environment
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* **Install:** To import `SMPL_Layer` in another project with `from smplpytorch.pytorch.smpl_layer import SMPL_Layer` do one of the following.
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* Option 1: This should automatically install the dependencies.
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``` bash
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git clone https://github.com/gulvarol/smplpytorch.git
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cd smplpytorch
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pip install .
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```
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* Option 2: You can install `smplpytorch` from [PyPI](https://pypi.org/project/smplpytorch/). Additionally, you might need to install [chumpy](https://github.com/hassony2/chumpy.git).
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``` bash
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pip install smplpytorch
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```
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### 2. Download SMPL pickle files
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* Download the models from the [SMPL website](http://smpl.is.tue.mpg.de/) by choosing "SMPL for Python users". Note that you need to comply with the [SMPL model license](http://smpl.is.tue.mpg.de/license_model).
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* Extract and copy the `models` folder into the `smplpytorch/native/` folder (or set the `model_root` parameter accordingly).
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### 3. Download Dataset
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- Download the datasets you want to fit
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currently supported datasets:
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- [HumanAct12](https://ericguo5513.github.io/action-to-motion/)
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- [UTD-MHAD](https://personal.utdallas.edu/~kehtar/UTD-MHAD.html)
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- Set the **DATASET.PATH** in the corresponding configuration file to the location of dataset.
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## Fitting
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### 1. Executing Code
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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):
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```
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python fit/tools/main.py --dataset_name [DATASET NAME] --dataset_path [DATASET PATH]
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```
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### 2. Output
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- **Direction**: The output SMPL parameters will be stored in *fit/output*
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- **Format:** The output are *.pkl* files, and the data format is:
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```
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{
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"label": [The label of action],
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"pose_params": pose parameters of SMPL (shape = [frame_num, 72]),
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"shape_params": pose parameters of SMPL (shape = [frame_num, 10]),
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"Jtr": key-point coordinates of SMPL model (shape = [frame_num, 24, 3])
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}
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```
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