SOMA genesis

LICENSE

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+License
+
+Software Copyright License for non-commercial scientific research purposes
+
+Please read carefully the following terms and conditions and any accompanying documentation before you download and/or
+use the SOMA data and software, (the "Data & Software"),
+including 3D meshes, images, videos, textures, software, scripts, and animations.
+By downloading and/or using the Data & Software
+(including downloading, cloning, installing, and any other use of the corresponding github repository),
+you acknowledge that you have read these terms and conditions, understand them, and agree to be bound by them.
+If you do not agree with these terms and conditions, you must not download and/or use the Data & Software.
+Any infringement of the terms of this agreement will automatically terminate your rights under this License.
+
+Ownership / Licensees
+The Data & Software and the associated materials has been developed at the
+Max Planck Institute for Intelligent Systems (hereinafter “MPI”).
+Any copyright or patent right is owned by and proprietary material of the
+Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (hereinafter “MPG”; MPI and MPG hereinafter collectively “Max-Planck” or “Licensor”).
+
+License Grant
+Licensor grants you (Licensee) personally a single-user, non-exclusive, non-transferable, free of charge right:
+To install the Data & Software on computers owned, leased or otherwise controlled by you and/or your organization;
+To use the Data & Software for the sole purpose of performing non-commercial scientific research, non-commercial education, or non-commercial artistic projects;
+Any other use, in particular any use for commercial, pornographic, military, or surveillance, purposes is prohibited.
+This includes, without limitation, incorporation in a commercial product, use in a commercial service, or production of
+other artefacts for commercial purposes. The Data & Software may not be used to create fake, libelous, misleading, or
+defamatory content of any kind excluding analyses in peer-reviewed scientific research. The Data & Software may not be
+reproduced, modified and/or made available in any form to any third party without Max-Planck’s prior written permission.
+
+The Data & Software may not be used for pornographic purposes or to generate pornographic material whether commercial or not.
+This license also prohibits the use of the Data & Software to train methods/algorithms/neural networks/etc.
+for commercial, pornographic, military, surveillance, or defamatory use of any kind.
+By downloading the Data & Software, you agree not to reverse engineer it.
+
+No Distribution
+The Data & Software and the license herein granted shall not be copied, shared, distributed, re-sold, offered for re-sale, transferred or sub-licensed in whole or in part except that you may make one copy for archive purposes only.
+
+Disclaimer of Representations and Warranties
+You expressly acknowledge and agree that the Data & Software results from basic research, is provided “AS IS”, may contain errors, and that any use of the Data & Software is at your sole risk.
+LICENSOR MAKES NO REPRESENTATIONS OR WARRANTIES OF ANY KIND CONCERNING THE DATA & SOFTWARE, NEITHER EXPRESS NOR IMPLIED, AND THE ABSENCE OF ANY LEGAL OR ACTUAL DEFECTS, WHETHER DISCOVERABLE OR NOT.
+Specifically, and not to limit the foregoing, licensor makes no representations or warranties
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+(ii) that the use of the Data & Software will not infringe any patents, copyrights or other intellectual property rights of a third party, and
+(iii) that the use of the Data & Software will not cause any damage of any kind to you or a third party.
+
+Limitation of Liability
+Because this Data & Software License Agreement qualifies as a donation, according to Section 521 of the
+German Civil Code (Bürgerliches Gesetzbuch – BGB) Licensor as a donor is liable for intent and gross negligence only.
+If the Licensor fraudulently conceals a legal or material defect, they are obliged to compensate the Licensee for the resulting damage.
+Licensor shall be liable for loss of data only up to the amount of typical recovery costs which would have arisen had proper
+ and regular data backup measures been taken. For the avoidance of doubt Licensor shall be liable in accordance with the
+ German Product Liability Act in the event of product liability. The foregoing applies also to Licensor’s legal representatives
+ or assistants in performance. Any further liability shall be excluded. Patent claims generated through the usage of the
+  Data & Software cannot be directed towards the copyright holders. The Data & Software is provided in the state of development
+  the licensor defines. If modified or extended by Licensee, the Licensor makes no claims about the fitness of the Data & Software and is not responsible for any problems such modifications cause.
+
+No Maintenance Services
+You understand and agree that Licensor is under no obligation to provide either maintenance services, update services, notices of latent defects, or corrections of defects with regard to the Data & Software. Licensor nevertheless reserves the right to update, modify, or discontinue the Data & Software at any time. Defects of the Data & Software must be notified in writing to the Licensor with a comprehensible description of the error symptoms. The notification of the defect should enable the reproduction of the error. The Licensee is encouraged to communicate any use, results, modification or publication.
+
+Subjects' Consent
+All subjects gave informed written consent to share their data for research purposes.
+You further agree to delete data or change their use, in case a subject changes or withdraws their consent.
+
+Publications using the Data & Software
+You acknowledge that the Data & Software is a valuable scientific resource and agree to appropriately reference the following paper in any publication making use of the Data & Software:
+
+@inproceedings{SOMA:ICCV:2021,
+  title = {{SOMA}: Solving Optical Marker-Based MoCap Automatically},
+  author = {Ghorbani, Nima and Black, Michael J.},
+  booktitle = {Proceedings of IEEE/CVF International Conference on Computer Vision (ICCV)},
+  month = oct,
+  year = {2021},
+  doi = {},
+  month_numeric = {10}}
+
+Commercial licensing opportunities
+For commercial uses of the Data & Software, please send email to [email protected]
+
+This Agreement shall be governed by the laws of the Federal Republic of Germany except for the UN Sales Convention.

MANIFEST.in

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+recursive-include  support_data *.npz
+recursive-include  support_data *.ply
+recursive-include  support_data/conf *.yaml
+# added by check-manifest
+include *.txt
+recursive-include src *.cpp
+recursive-include src *.h
+recursive-include src *.md
+recursive-include src *.py
+recursive-include src *.pyx
+recursive-include src *.txt
+recursive-include src *.whl
+recursive-include src Makefile

README.md

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-# soma
-Solving Optical MoCap Automatically
+# SOMA: Solving Optical Marker-Based MoCap Automatically, ICCV'21
+
+This repository contains the official PyTorch implementation of:
+
+SOMA: Solving Optical Marker-Based MoCap Automatically\
+Nima Ghorbani and Michael J. Black\
+Full paper | Video | Project website | Poster
+
+![alt text](https://download.is.tue.mpg.de/soma/tutorials/soma_github_teaser.gif "mocap point clouds (black dots in the back) turned into labeled markers (colored dots)")
+
+SOMA **automatically transforms raw marker-based mocap point clouds** (black dots in the back) into **labeled markers** (colored dots) and **solved bodies**.
+
+## Installation
+
+SOMA is originally developed in Python 3.7, PyTorch 1.8.2 LTS, for Ubuntu 20.04.2 LTS. 
+Below we prepare the python environment using [Anaconda](https://www.anaconda.com/products/individual), 
+however, we opt for a simple pip package manager for installing dependencies.
+
+````
+sudo apt install libatlas-base-dev
+sudo apt install libpython3.7
+sudo apt install libtbb2
+
+conda create -n soma python=3.7 
+conda install -c conda-forge ezc3d
+
+pip3 install torch==1.8.2+cu102 torchvision==0.9.2+cu102 torchaudio==0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
+
+````
+ezc3d installation is currently not supported by pip.
+
+Assuming that you have already cloned this repository to your local drive 
+go to the root directory of SOMA code and run
+````
+pip install -r requirements.txt
+python setup.py develop
+````
+Copy the precompiled 
+[smpl-fast-derivatives](https://download.is.tue.mpg.de/download.php?domain=soma&sfile=smpl-fast-derivatives.tar.bz2) 
+into your python site-packages folder, i.e. ````anaconda3/envs/soma/lib/python3.7/site-packages````.
+The final directory should look like ````anaconda3/envs/soma/lib/python3.7/site-packages/psbody/smpl````.
+
+Install the psbody.mesh library following the instructions in [https://github.com/MPI-IS/mesh](https://github.com/MPI-IS/mesh).
+Hint: clone the mesh repository and run the following from the anaconda environment:  ````python setup.py install ````.
+
+To use the rendering capabilities first install an instance of Blender-2.83 LTS on your machine.
+Afterward uncompress contents of the precompiled 
+[bpy-2.83](https://download.is.tue.mpg.de/download.php?domain=soma&sfile=blender/bpy-2.83-20200908.tar.bz2) 
+into your python site-packages folder, i.e. ````anaconda3/envs/soma/lib/python3.7/site-packages````. 
+
+Last but not least, the current SOMA code relies on [MoSh++](https://amass.is.tue.mpg.de/) mocap solver. 
+Please install [MoSh++](https://github.com/nghorbani/moshpp) following the guidelines in its repository.
+
+
+## Using SOMA
+There are multiple main parts of the codebase that we try to explain in the [Tutorials](src/tutorials):
+- Training SOMA
+- Running SOMA on MPC data
+- Running MoSh++ on labeled mocaps 
+- Rendering reconstructed bodies with [Blender](https://docs.blender.org/api/current/info_quickstart.html)
+- Evaluating SOMA; i.e. labeling performance and surface reconstruction accuracy
+
+## Citation
+
+Please cite the following paper if you use this code directly or indirectly in your research/projects:
+
+```
+@inproceedings{SOMA:ICCV:2021,
+  title = {{SOMA}: Solving Optical Marker-Based MoCap Automatically},
+  author = {Ghorbani, Nima and Black, Michael J.},
+  booktitle = {Proceedings of IEEE/CVF International Conference on Computer Vision (ICCV)},
+  month = oct,
+  year = {2021},
+  doi = {},
+  month_numeric = {10}}
+```
+
+## License
+
+Software Copyright License for **non-commercial scientific research purposes**. Please read carefully
+the [terms and conditions](./LICENSE) and any accompanying documentation before you download and/or
+use the SOMA data and software, (the "Data & Software"), software, scripts, and animations. 
+By downloading and/or using the Data & Software (including downloading, cloning, installing, and any other use of this repository), 
+you acknowledge that you have read these terms
+and conditions, understand them, and agree to be bound by them. If you do not agree with these terms and conditions, you
+must not download and/or use the Data & Software. 
+Any infringement of the terms of this agreement will automatically terminate
+your rights under this [License](./LICENSE).
+
+## Contact
+
+The code in this repository is developed by [Nima Ghorbani](https://nghorbani.github.io/) 
+while at [Max-Planck Institute for Intelligent Systems, Tübingen, Germany](https://is.mpg.de/person/nghorbani).

requirements.txt

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+git+https://github.com/nghorbani/human_body_prior.git@SOMA
+git+https://github.com/nghorbani/body_visualizer
+git+https://github.com/nghorbani/configer.git
+imageio
+mkl
+mkl-service
+mkl_fft
+mkl_random
+numpy
+pyOpenSSL
+pillow
+scikit-image
+scipy
+setuptools
+loguru
+omegaconf
+six
+tk
+toolz
+wheel
+pytorch3d
+notifiers
+tqdm
+opencv-python
+c3d
+chumpy
+ipython
+markdown
+omegaconf
+pandas
+pycodestyle
+pytorch-lightning
+scikit-learn
+tensorboard
+threadpoolctl
+setuptools
+trimesh
+colour
+seaborn
+tables
+transforms3d
+xlsxwriter
+jupyterlab

setup.py

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+# -*- coding: utf-8 -*-
+#
+# Copyright (C) 2021 Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG),
+# acting on behalf of its Max Planck Institute for Intelligent Systems and the
+# Max Planck Institute for Biological Cybernetics. All rights reserved.
+#
+# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is holder of all proprietary rights
+# on this computer program. You can only use this computer program if you have closed a license agreement
+# with MPG or you get the right to use the computer program from someone who is authorized to grant you that right.
+# Any use of the computer program without a valid license is prohibited and liable to prosecution.
+# Contact: [email protected]
+#
+# If you use this code in a research publication please cite the following:
+#
+# @inproceedings{SOMA:ICCV:2021,
+#   title = {{SOMA}: Solving Optical MoCap Automatically},
+#   author = {Ghorbani, Nima and Black, Michael J.},
+#   booktitle = {Proceedings of IEEE/CVF International Conference on Computer Vision (ICCV)},
+#   month = oct,
+#   year = {2021},
+#   doi = {},
+#   month_numeric = {10}}
+#
+# You can find complementary content at the project website: https://soma.is.tue.mpg.de/
+#
+# Code Developed by:
+# Nima Ghorbani <https://nghorbani.github.io/>
+# While at Max-Planck Institute for Intelligent Systems, Tübingen, Germany
+#
+# 2021.06.18
+from setuptools import setup, find_packages
+from glob import glob
+
+setup(name='soma',
+      version='5.0.0',
+      packages=find_packages('src'),
+      package_dir={'': 'src'},
+      include_package_data=True,
+      data_files=[
+          ('soma/support_data', glob('support_data/*.*')),
+          ('soma/support_data/conf/parallel_conf', glob('support_data/conf/parallel_conf/*.*')),
+          ('soma/support_data/github_files', glob('support_data/github_files/*.*')),
+          ('soma/support_data/tests', glob('support_data/tests/*.*')),
+                  ('soma/support_data/conf', glob('support_data/conf/*.*'))
+      ],
+
+      author='Nima Ghorbani',
+      author_email='[email protected]',
+      maintainer='Nima Ghorbani',
+      maintainer_email='[email protected]',
+      url='https://github.com/nghorbani/soma',
+      description='Solving Optical Marker-Based Motion Capture Automatically',
+      license='See LICENSE.txt',
+      long_description=open("README.md").read(),
+      long_description_content_type="text/markdown",
+      install_requires=[],
+      dependency_links=[],
+      classifiers=[
+          "Intended Audience :: Research",
+          "Natural Language :: English",
+          "Operating System :: POSIX",
+          "Operating System :: POSIX :: BSD",
+          "Operating System :: POSIX :: Linux",
+          "Programming Language :: Python",
+          "Programming Language :: Python :: 3",
+          "Programming Language :: Python :: 3.7", ],
+      )