OpenPose is a library for real-time multi-person keypoint detection and multi-threading written in C++ using OpenCV and Caffe.
- Sep 2017: CMake installer and IP camera support!
- Jul 2017: Windows portable demo!
- Jul 2017: Hands released!
- Jun 2017: Face released!
- May 2017: Windows version!
- Apr 2017: Body released!
- Check all the release notes.
- Latest News
- Results
- Introduction
- Functionality
- Installation, Reinstallation and Uninstallation
- Quick Start
- Output
- Standalone Face Or Hand Keypoint Detector
- Speed Up Openpose And Benchmark
- Send Us Failure Cases!
- Send Us Your Feedback!
- Citation
- Other Contributors
OpenPose represents the first real-time system to jointly detect human body, hand and facial keypoints (in total 130 keypoints) on single images. In addition, the system computational performance on body keypoint estimation is invariant to the number of detected people in the image. It uses Caffe, but it could easily be ported to other frameworks (Tensorflow, Torch, etc.). If you implement any of those, feel free to make a pull request!
OpenPose is authored by Gines Hidalgo, Zhe Cao, Tomas Simon, Shih-En Wei, Hanbyul Joo and Yaser Sheikh. In addition, it is being maintained by Gines Hidalgo and Bikramjot Hanzra.
It 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 link. For commercial queries, contact Yaser Sheikh.
In addition, OpenPose would not be possible without the CMU Panoptic Studio.
The pose estimation work is based on the C++ code from the ECCV 2016 demo, "Realtime Multiperson Pose Estimation", Zhe Cao, Tomas Simon, Shih-En Wei, Yaser Sheikh. The original repo includes Matlab and Python version, as well as the training code.
- Multi-person 15 or 18-keypoint body pose estimation and rendering. Running time invariant to number of people on the image.
- Multi-person 2x21-keypoint hand estimation and rendering. Note: In this initial version, running time linearly depends on the number of people on the image.
- Multi-person 70-keypoint face estimation and rendering. Note: In this initial version, running time linearly depends on the number of people on the image.
- Flexible and easy-to-configure multi-threading module.
- Image, video, webcam and IP camera reader.
- Able to save and load the results in various formats (JSON, XML, PNG, JPG, ...).
- Small display and GUI for simple result visualization.
- All the functionality is wrapped into a simple-to-use OpenPose Wrapper class.
You can find the installation, reinstallation and uninstallation steps on: doc/installation.md.
Most users cases should not need to dive deep into the library, they might just be able to use the Demo or the simple OpenPose Wrapper. So you can most probably skip the library details in OpenPose Library.
Your case if you just want to process a folder of images or video or webcam and display or save the pose results.
Forget about the OpenPose library details and just read the doc/demo_overview.md 1-page section.
Your case if you want to read a specific format of image source and/or add a specific post-processing function and/or implement your own display/saving.
(Almost) forget about the library, just take a look to the Wrapper tutorial on examples/tutorial_wrapper/.
Note: you should not need to modify the OpenPose source code nor examples. In this way, you are able to directly upgrade OpenPose anytime in the future without changing your code. You might create your custom code on examples/user_code/ and compile it by using make all in the OpenPose folder.
Your case if you want to change internal functions and/or extend its functionality. First, take a look at the Demo and OpenPose Wrapper. Second, read the 2 following subsections: OpenPose Overview and Extending Functionality.
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OpenPose Overview: Learn the basics about the library source code in doc/library_overview.md.
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Extending Functionality: Learn how to extend the library in doc/library_extend_functionality.md.
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Adding An Extra Module: Learn how to add an extra module in doc/library_add_new_module.md.
Check the output (format, keypoint index ordering, etc.) in doc/output.md.
If you do not need the body keypoint detector and want to considerably speed up the face keypoint detection, you can use the new approach based on OpenCV face detector. See doc/standalone_face_or_hand_keypoint_detector.md.
You can also use the OpenPose hand and/or face keypoint detectors with your own face or hand detectors, rather than using the body keypoint detector as initial detector for those. E.g. in case of hand camera views at which the hands are visible but not the rest of the body, so that the OpenPose detector would fail. See doc/standalone_face_or_hand_keypoint_detector.md.
Check the OpenPose Benchmark and some hints to speed up OpenPose on doc/installation.md#faq.
If you find videos or images where OpenPose does not seems to work well, feel free to send them to [email protected], we will use them to improve the quality of the algorithm. Thanks!
Our library is open source for research purposes, and we want to continuously improve it! So please, let us know if...
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... you find any bug (in functionality or speed).
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... you added some functionality to some class or some new Worker subclass which we might potentially incorporate.
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... you know how to speed up or improve any part of the library.
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... you have a request about possible functionality.
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... etc.
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 face keypoint detector was trained using the same procedure described in [Simon et al. 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{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{wei2016cpm,
author = {Shih-En Wei and Varun Ramakrishna and Takeo Kanade and Yaser Sheikh},
booktitle = {CVPR},
title = {Convolutional pose machines},
year = {2016}
}
We would like to thank all the people who helped OpenPose in any way. The main contributors are listed in doc/contributors.md.