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README.md

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+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>
+
+## 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.