xrmocap

OpenXRLab Multi-view Motion Capture Toolbox and Benchmark

https://github.com/openxrlab/xrmocap

Science Score: 54.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
Found CITATION.cff file
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codemeta.json file
Found codemeta.json file
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Links to: arxiv.org, ieee.org
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Scientific vocabulary similarity
Low similarity (14.9%) to scientific vocabulary

Keywords

human-parametric-model motion-capture python smpl smpl-x

Last synced: 6 months ago · JSON representation ·

Repository

OpenXRLab Multi-view Motion Capture Toolbox and Benchmark

Basic Info

Host: GitHub
Owner: openxrlab
License: other
Language: Python
Default Branch: main
Homepage: https://xrmocap.readthedocs.io/
Size: 1.54 MB

Statistics

Stars: 384
Watchers: 10
Forks: 44
Open Issues: 9
Releases: 4

Topics

human-parametric-model motion-capture python smpl smpl-x

Created over 3 years ago · Last pushed 8 months ago

Metadata Files

Readme Contributing License Code of conduct Citation

[![Documentation](https://readthedocs.org/projects/xrmocap/badge/?version=latest)](https://xrmocap.readthedocs.io/en/latest/?badge=latest) [![actions](https://github.com/openxrlab/xrmocap/workflows/build/badge.svg)](https://github.com/openxrlab/xrmocap/actions) [![codecov](https://codecov.io/gh/openxrlab/xrmocap/branch/main/graph/badge.svg)](https://codecov.io/gh/openxrlab/xrmocap) [![PyPI](https://img.shields.io/pypi/v/xrmocap)](https://pypi.org/project/xrmocap/) [![Percentage of issues still open](https://isitmaintained.com/badge/open/openxrlab/xrmocap.svg)](https://github.com/openxrlab/xrmocap/issues)

Introduction

English | 简体中文

XRMoCap is an open-source PyTorch-based codebase for the use of multi-view motion capture. It is a part of the OpenXRLab project.

If you are interested in single-view motion capture, please refer to mmhuman3d for more details.

https://user-images.githubusercontent.com/26729379/187710195-ba4660ce-c736-4820-8450-104f82e5cc99.mp4

A detailed introduction can be found in introduction.md.

Major Features

Support popular multi-view motion capture methods for single person and multiple people

XRMoCap reimplements SOTA multi-view motion capture methods, ranging from single person to multiple people. It supports an arbitrary number of calibrated cameras greater than 2, and provides effective strategies to automatically select cameras.

Support keypoint-based and parametric human model-based multi-view motion capture algorithms

XRMoCap supports two mainstream motion representations, keypoints3d and SMPL(-X) model, and provides tools for conversion and optimization between them.

Integrate optimization-based and learning-based methods into one modular framework

XRMoCap decomposes the framework into several components, based on which optimization-based and learning-based methods are integrated into one framework. Users can easily prototype a customized multi-view mocap pipeline by choosing different components in configs.

News

2022-12-21: XRMoCap v0.7.0 is released. Major updates include:
- Add mviewmpersonend2end_estimator for learning-based method
- Add SMPLX support and allow smpldata initiation in `mviewspersonsmplestimator`
- Add multiple optimizers, detailed joint weights and priors, grad clipping for better SMPLify results
- Add mediapipe_estimator for human keypoints2d perception
2022-10-14: XRMoCap v0.6.0 is released. Major updates include:
- Add 4D Association Graph, the first Python implementation to reproduce this algorithm
- Add Multi-view multi-person top-down smpl estimation
- Add reprojection error point selector
2022-09-01: XRMoCap v0.5.0 is released. Major updates include:
- Support HuMMan Mocap toolchain for multi-view single person SMPL estimation
- Reproduce MvP, a deep-learning-based SOTA for multi-view multi-human 3D pose estimation
- Reproduce MVPose (single frame) and MVPose (temporal tracking and filtering), two optimization-based methods for multi-view multi-human 3D pose estimation
- Support SMPLify, SMPLifyX, SMPLifyD and SMPLifyXD

Benchmark

More details can be found in benchmark.md.

Supported methods:

(click to collapse)

- [x] [SMPLify](https://smplify.is.tue.mpg.de/) (ECCV'2016) - [x] [SMPLify-X](https://smpl-x.is.tue.mpg.de/) (CVPR'2019) - [x] [MVPose (Single frame)](https://zju3dv.github.io/mvpose/) (CVPR'2019) - [x] [MVPose (Temporal tracking and filtering)](https://zju3dv.github.io/mvpose/) (T-PAMI'2021) - [x] [Shape-aware 3D Pose Optimization](https://ait.ethz.ch/projects/2021/multi-human-pose/) (ICCV'2019) - [x] [MvP](https://arxiv.org/pdf/2111.04076.pdf) (NeurIPS'2021) - [x] [HuMMan MoCap](https://caizhongang.github.io/projects/HuMMan/) (ECCV'2022) - [x] [4D Association Graph](http://www.liuyebin.com/4dassociation/) (CVPR'2020)

Supported datasets:

(click to collapse)

- [x] [Campus](https://campar.in.tum.de/Chair/MultiHumanPose) (CVPR'2014) - [x] [Shelf](https://campar.in.tum.de/Chair/MultiHumanPose) (CVPR'2014) - [x] [CMU Panoptic](http://domedb.perception.cs.cmu.edu/) (ICCV'2015) - [x] [4D Association](https://github.com/zhangyux15/multiview_human_dataset) (CVPR'2020)

Getting Started

Please see getting_started.md for the basic usage of XRMoCap.

License

The license of our codebase is Apache-2.0. Note that this license only applies to code in our library, the dependencies of which are separate and individually licensed. We would like to pay tribute to open-source implementations to which we rely on. Please be aware that using the content of dependencies may affect the license of our codebase. Refer to LICENSE to view the full license.

Citation

If you find this project useful in your research, please consider cite:

bibtex @misc{xrmocap, title={OpenXRLab Multi-view Motion Capture Toolbox and Benchmark}, author={XRMoCap Contributors}, howpublished = {\url{https://github.com/openxrlab/xrmocap}}, year={2022} }

Contributing

We appreciate all contributions to improve XRMoCap. Please refer to CONTRIBUTING.md for the contributing guideline.

Acknowledgement

XRMoCap is an open source project that is contributed by researchers and engineers from both the academia and the industry. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new models.

Projects in OpenXRLab

XRPrimer: OpenXRLab foundational library for XR-related algorithms.
XRSLAM: OpenXRLab Visual-inertial SLAM Toolbox and Benchmark.
XRSfM: OpenXRLab Structure-from-Motion Toolbox and Benchmark.
XRLocalization: OpenXRLab Visual Localization Toolbox and Server.
XRMoCap: OpenXRLab Multi-view Motion Capture Toolbox and Benchmark.
XRMoGen: OpenXRLab Human Motion Generation Toolbox and Benchmark.
XRNeRF: OpenXRLab Neural Radiance Field (NeRF) Toolbox and Benchmark.
XRFeitoria: OpenXRLab Synthetic Data Rendering Toolbox.
XRViewer: OpenXRLab Data Visualization Toolbox.
XRTailor: OpenXRLab GPU Cloth Simulator.

Owner

Name: OpenXRLab
Login: openxrlab
Kind: organization

Website: https://openxrlab.org.cn/
Twitter: OpenXRLab
Repositories: 11
Profile: https://github.com/openxrlab

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - name: "XRMoCap Contributors"
title: "XRMoCap: OpenXRLab Multi-view Motion Capture Toolbox and Benchmark"
date-released: 2022-09-01
url: "https://github.com/openxrlab/xrmocap"
license: Apache-2.0

GitHub Events

Total

Issues event: 23
Watch event: 40
Issue comment event: 1
Push event: 1
Pull request review comment event: 1
Pull request review event: 3
Pull request event: 4
Fork event: 4

Last Year

Issues event: 23
Watch event: 40
Issue comment event: 1
Push event: 1
Pull request review comment event: 1
Pull request review event: 3
Pull request event: 4
Fork event: 4

Committers

Last synced: almost 3 years ago

All Time

Total Commits: 81
Total Committers: 12
Avg Commits per committer: 6.75
Development Distribution Score (DDS): 0.667

Top Committers

Name	Email	Commits
LazyBusyYang	g**n@o**m	27
wqyin	3**n@u**m	12
Lei Yang	y**v@g**m	11
LazyBusyYang	g**3@s**m	7
jiaqiAA	6**A@u**m	7
jiaqiAA	l**0@g**m	5
Kanghao Chen	2**9@q**m	4
wqyin	w**5@g**m	3
Yamato-01	8**1@u**m	2
tonylu0728	4**8@u**m	1
Kanghao Chen	k**7@g**m	1
WEI CHEN	7**b@u**m	1

Committer Domains (Top 20 + Academic)

qq.com: 1 sensetime.com: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 86
Total pull requests: 77
Average time to close issues: 2 months
Average time to close pull requests: 6 days
Total issue authors: 34
Total pull request authors: 14
Average comments per issue: 1.37
Average comments per pull request: 0.94
Merged pull requests: 60
Bot issues: 35
Bot pull requests: 0

Past Year

Issues: 15
Pull requests: 3
Average time to close issues: 3 months
Average time to close pull requests: 3 days
Issue authors: 4
Pull request authors: 2
Average comments per issue: 0.13
Average comments per pull request: 0.0
Merged pull requests: 3
Bot issues: 12
Bot pull requests: 0

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Top Authors

Issue Authors

github-actions[bot] (35)
aichunling0418 (10)
LazyBusyYang (5)
Taylorminer (3)
patrickESM (2)
Billccx (2)
3huo (2)
jiku100 (1)
rorrewang (1)
Dragon2938734 (1)
Young2647 (1)
YaoBeiji (1)
Charrrrrlie (1)
canghaiyunfan (1)
yl-1993 (1)

Pull Request Authors

LazyBusyYang (21)
wqyin (16)
jiaqiAA (11)
yl-1993 (7)
KHao123 (7)
Yamato-01 (3)
WYK96 (3)
ghost (2)
Era-Dorta-TU-Delft (2)
Wei-Chen-hub (1)
aichunling0418 (1)
danielrogel (1)
caizhongang (1)
tonylu0728 (1)

Top Labels

Issue Labels

inactive (36) enhancement (2) bug (1)

Pull Request Labels

Packages

Total packages: 1
Total downloads:
- pypi 18 last-month

Total dependent packages: 0
Total dependent repositories: 0
Total versions: 5
Total maintainers: 3

pypi.org: xrmocap

Homepage: https://github.com/openxrlab/xrmocap
Documentation: https://xrmocap.readthedocs.io/
License: Apache License 2.0
Latest release: 0.8.0
published almost 3 years ago

Versions: 5
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 18 Last month

Rankings

Stargazers count: 5.5%

Dependent packages count: 6.6%

Forks count: 8.9%

Average: 14.3%

Downloads: 20.0%

Dependent repos count: 30.6%

Maintainers (3)

yl-1993 openxrlab LasyBusyYang

Last synced: 6 months ago

Dependencies

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requirements/test.txt pypi

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requirements/readthedocs.txt pypi

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dockerfiles/runtime_ubt18/Dockerfile docker

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setup.py pypi

xrmocap

Science Score: 54.0%

Keywords

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

Introduction

Major Features

News

Benchmark

Getting Started

License

Citation

Contributing

Acknowledgement

Projects in OpenXRLab

Owner

Citation (CITATION.cff)

GitHub Events

Total

Last Year

Committers

All Time

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

pypi.org: xrmocap

Rankings

Maintainers (3)

Dependencies