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OpenPose represents the first real-time multi-person system to jointly detect human body, hand, facial, and foot keypoints (in total 135 keypoints) on single images.
It is authored by Gines Hidalgo, Zhe Cao, Tomas Simon, Shih-En Wei, Hanbyul Joo, and Yaser Sheikh. Currently, it is being maintained by Gines Hidalgo and Yaadhav Raaj. In addition, OpenPose would not be possible without the CMU Panoptic Studio dataset. We would also like to thank all the people who helped OpenPose in any way. The main contributors are listed in doc/contributors.md.
Authors Gines Hidalgo (left) and Hanbyul Joo (right) in front of the CMU Panoptic Studio
For further details, check all released features and release notes.
Testing the Crazy Uptown Funk flashmob in Sydney video sequence with OpenPose
Testing the 3D Reconstruction Module of OpenPose
Authors Gines Hidalgo (left image) and Tomas Simon (right image) testing OpenPose
Tianyi Zhao and Gines Hidalgo testing their OpenPose Unity Plugin
Inference time comparison between the 3 available pose estimation libraries: OpenPose, Alpha-Pose (fast Pytorch version), and Mask R-CNN:
This analysis was performed using the same images for each algorithm and a batch size of 1. Each analysis was repeated 1000 times and then averaged. This was all performed on a system with a Nvidia 1080 Ti and CUDA 8. Megvii (Face++) and MSRA GitHub repositories were excluded because they only provide pose estimation results given a cropped person. However, they suffer the same problem than Alpha-Pose and Mask R-CNN, their runtimes grow linearly with the number of people.
Windows portable version: Simply download and use the latest version from the Releases section.
Otherwise, check doc/installation.md for instructions on how to build OpenPose from source.
Most users do not need the OpenPose C++/Python API, but can simply use the OpenPose Demo:
# Ubuntu
./build/examples/openpose/openpose.bin --video examples/media/video.avi
:: Windows - Portable Demo
bin\OpenPoseDemo.exe --video examples\media\video.avi
Calibration toolbox: To easily calibrate your cameras for 3-D OpenPose or any other stereo vision task. See doc/modules/calibration_module.md.
OpenPose C++ API: If you want to read a specific input, and/or add your custom post-processing function, and/or implement your own display/saving, check the C++ API tutorial on examples/tutorial_api_cpp/ and doc/library_introduction.md. You can create your custom code on examples/user_code/ and quickly compile it with CMake when compiling the whole OpenPose project. Quickly add your custom code: See examples/user_code/README.md for further details.
OpenPose Python API: Analogously to the C++ API, find the tutorial for the Python API on examples/tutorial_api_python/.
Adding an extra module: Check doc/library_add_new_module.md.
Standalone face or hand detector:
Output (format, keypoint index ordering, etc.) in doc/output.md.
Check the OpenPose Benchmark as well as some hints to speed up and/or reduce the memory requirements for OpenPose on doc/speed_up_openpose.md.
For training OpenPose, check github.com/CMU-Perceptual-Computing-Lab/openpose_train.
For the foot dataset, check the foot dataset website and new OpenPose paper for more information.
Our library is open source for research purposes, and we want to continuously improve it! So please, let us know if...
Just comment on GitHub or make a pull request and we will answer as soon as possible! Send us an email if you use the library to make a cool demo or YouTube video!
Please cite these papers in your publications if it helps your research. The body-foot model and any additional functionality (calibration, 3-D reconstruction, etc.) use [Cao et al. 2018]
; the hand and face keypoint detectors use [Cao et al. 2018]
and [Simon et al. 2017]
(the face detector was trained using the same procedure than for hands); and the old (deprecated) body-only model uses [Cao et al. 2017]
.
@inproceedings{cao2018openpose,
author = {Zhe Cao and Gines Hidalgo and Tomas Simon and Shih-En Wei and Yaser Sheikh},
booktitle = {arXiv preprint arXiv:1812.08008},
title = {Open{P}ose: realtime multi-person 2{D} pose estimation using {P}art {A}ffinity {F}ields},
year = {2018}
}
@inproceedings{simon2017hand,
author = {Tomas Simon and Hanbyul Joo and Iain Matthews and Yaser Sheikh},
booktitle = {CVPR},
title = {Hand Keypoint Detection in Single Images using Multiview Bootstrapping},
year = {2017}
}
@inproceedings{cao2017realtime,
author = {Zhe Cao and Tomas Simon and Shih-En Wei and Yaser Sheikh},
booktitle = {CVPR},
title = {Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields},
year = {2017}
}
@inproceedings{wei2016cpm,
author = {Shih-En Wei and Varun Ramakrishna and Takeo Kanade and Yaser Sheikh},
booktitle = {CVPR},
title = {Convolutional pose machines},
year = {2016}
}
Links to the papers:
OpenPose is freely available for free non-commercial use, and may be redistributed under these conditions. Please, see the license for further details. Interested in a commercial license? Check this FlintBox link. For commercial queries, use the Directly Contact Organization
section from the FlintBox link and also send a copy of that message to Yaser Sheikh.
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