Skip to main content

SMPL human body layer for PyTorch is a differentiable PyTorch layer

Project description

SMPL layer for PyTorch

SMPL human body [1] layer for PyTorch (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 repository by Yana Hasson.

smpl

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).

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 repository from Yana Hasson, which implements the MANO hand model [2] layer.

The code is a PyTorch port of the original SMPL model from chumpy. It builds on the work of Loper et al. [1].

The code reuses part of the code by Zhang Xiong to compute the rotation utilities.

If you find this code useful for your research, please cite the original SMPL 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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

smplpytorch-0.0.8.tar.gz (9.5 kB view details)

Uploaded Source

Built Distribution

smplpytorch-0.0.8-py3-none-any.whl (22.9 kB view details)

Uploaded Python 3

File details

Details for the file smplpytorch-0.0.8.tar.gz.

File metadata

  • Download URL: smplpytorch-0.0.8.tar.gz
  • Upload date:
  • Size: 9.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.2 CPython/3.7.3

File hashes

Hashes for smplpytorch-0.0.8.tar.gz
Algorithm Hash digest
SHA256 35a011206985c993dccea63e7a4d77bc71369baa0fd58f36ebcad6da3af38779
MD5 027b5c776b07f44d4cc6c587ef8d8b65
BLAKE2b-256 16b30ea0c906ce4236efed823e0469fa1f9dfa330f05fc42d36bfbdf25c37f2f

See more details on using hashes here.

File details

Details for the file smplpytorch-0.0.8-py3-none-any.whl.

File metadata

  • Download URL: smplpytorch-0.0.8-py3-none-any.whl
  • Upload date:
  • Size: 22.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.2 CPython/3.7.3

File hashes

Hashes for smplpytorch-0.0.8-py3-none-any.whl
Algorithm Hash digest
SHA256 059a398a3f10acf74f8da7fca8bba870f6504523cd3df6f52264b496c37b0a4a
MD5 8cb607782625022d5bf769ff54c71429
BLAKE2b-256 5fc227a2e0739670c19c52da1f90ebb576c2d64eb93a8c397461db2a41365e9f

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page