/smg-smplx

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smg-smplx

teaser

This Python package provides a wrapper around relevant bits of SMPL-X. It also includes our method for fitting an SMPL body model to a 3D skeleton.

It is a submodule of smglib, the open-source Python framework associated with our drone research in the Cyber-Physical Systems group at the University of Oxford.

Installation (as part of smglib)

Note: Please read the top-level README for smglib before following these instructions.

  1. Open the terminal, and change to the <root>/smg-smplx directory.

  2. Check out the master branch.

  3. Activate the Conda environment, e.g. conda activate smglib.

  4. If you haven't already installed PyTorch, install it now. In our case, we did this via:

    pip install https://download.pytorch.org/whl/cu111/torch-1.9.1%2Bcu111-cp37-cp37m-win_amd64.whl
pip install https://download.pytorch.org/whl/torchaudio-0.9.1-cp37-cp37m-win_amd64.whl
pip install https://download.pytorch.org/whl/cu111/torchvision-0.10.1%2Bcu111-cp37-cp37m-win_amd64.whl

    However, you may need a different version of PyTorch for your system, so change this as needed. (In particular, the latest version will generally be ok.)

  5. Run pip install -e . at the terminal.

  6. Set up the data directory:

    i. Clone our fork of SMPL-X into C:/smplx, e.g.

    git clone git@github.com:sgolodetz/smplx.git C:/smplx

    Note: If you use a directory other than C:/smplx, you'll need to change the paths below.

    ii. Set (at a system level) the SMGLIB_SMPLX_DIR environment variable to point to C:/smplx.

    iii. Obtain the SMPL_MALE.pkl and SMPL_FEMALE.pkl files from here (you'll need to register), and put them in C:/smplx/models/smpl.

    iv. Obtain the SURREAL textures and put them in the C:/smplx/textures/smpl/surreal directory. For reference, the files you need are listed here, but you'll need to register to access them.

Publications

If you build on this framework for your research, please cite the following paper:

@inproceedings{Golodetz2022TR,
author = {Stuart Golodetz and Madhu Vankadari* and Aluna Everitt* and Sangyun Shin* and Andrew Markham and Niki Trigoni},
title = {{Real-Time Hybrid Mapping of Populated Indoor Scenes using a Low-Cost Monocular UAV}},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
month = {October},
year = {2022}
}

Acknowledgements

This work was supported by Amazon Web Services via the Oxford-Singapore Human-Machine Collaboration Programme, and by UKRI as part of the ACE-OPS grant. We would also like to thank Graham Taylor for the use of the Wytham Flight Lab, Philip Torr for the use of an Asus ZenFone AR, and Tommaso Cavallari for implementing TangoCapture.