Metadata-Version: 2.1
Name: smpl-pytorch
Version: 0.0.7
Summary: SMPL human body layer for PyTorch is a differentiable PyTorch layer
Home-page: https://github.com/gulvarol/smpl-pytorch
Author: Gul Varol
Author-email: gulvarols@gmail.com
License: UNKNOWN
Description: SMPL layer for PyTorch
        =======
        
        [SMPL](http://smpl.is.tue.mpg.de) human body [\[1\]](#references) layer for [PyTorch](https://pytorch.org/) (tested with v0.4 and v1.x)
        is a differentiable PyTorch layer that deterministically maps from pose and shape parameters to human body joints and vertices.
        It can be integrated into any architecture as a differentiable layer to predict body meshes.
        The code is adapted from the [manopth](https://github.com/hassony2/manopth) repository by [Yana Hasson](https://github.com/hassony2).
        
        <p align="center">
        <img src="image.png" alt="smpl" width="300"/>
        </p>
        
        ## Installation
        
        You can install smpl-pytorch from [PyPI](https://pypi.org/project/smpl-pytorch/):
        
            pip install smpl-pytorch
        
        Additionally, you have to download the SMPL pickle files:
          * 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).
          * Extract and copy the `models` folder into the `smpl/native/` folder.
        
        Alternatively, you can set up the package manually (see next).
        
        
        ## Setting up
        
        * Dependencies:
          * Install the dependencies listed in [environment.yml](environment.yml)
            * In an existing conda environment, `conda env update -f environment.yml`
            * In a new environment, `conda env create -f environment.yml`, will create a conda environment named `smplpytorch`
        * Download SMPL pickle files:
          * 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).
          * Extract and copy the `models` folder into the `smpl/native/` folder.
        
        ## Demo
        
        Forward pass the randomly created pose and shape parameters from the SMPL layer and display the human body mesh and joints:
        
        `python demo.py`
        
        ## Acknowledgements
        The code **largely** builds on the [manopth](https://github.com/hassony2/manopth) repository from [Yana Hasson](https://github.com/hassony2), which implements the [MANO](http://mano.is.tue.mpg.de) hand model [\[2\]](#references) layer.
        
        The code is a PyTorch port of the original [SMPL](http://smpl.is.tue.mpg.de) model from [chumpy](https://github.com/mattloper/chumpy). It builds on the work of [Loper](https://github.com/mattloper) et al. [\[1\]](#references).
        
        The code [reuses](https://github.com/gulvarol/smpl/pytorch/rodrigues_layer.py) [part of the code](https://github.com/MandyMo/pytorch_HMR/blob/master/src/util.py) by [Zhang Xiong](https://github.com/MandyMo) to compute the rotation utilities.
        
        If you find this code useful for your research, please cite the original [SMPL](http://smpl.is.tue.mpg.de) publication:
        
        ```
        @article{SMPL:2015,
            author = {Loper, Matthew and Mahmood, Naureen and Romero, Javier and Pons-Moll, Gerard and Black, Michael J.},
            title = {{SMPL}: A Skinned Multi-Person Linear Model},
            journal = {ACM Trans. Graphics (Proc. SIGGRAPH Asia)},
            number = {6},
            pages = {248:1--248:16},
            volume = {34},
            year = {2015}
        }
        ```
        
        ## References
        
        \[1\] Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J. Black, "SMPL: A Skinned Multi-Person Linear Model," SIGGRAPH Asia, 2015.
        
        \[2\] Javier Romero, Dimitrios Tzionas, and Michael J. Black, "Embodied Hands: Modeling and Capturing Hands and Bodies Together," SIGGRAPH Asia, 2017.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.5.0
Description-Content-Type: text/markdown
