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[**OpenPose**](https://github.com/CMU-Perceptual-Computing-Lab/openpose) has represented 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** [**Ginés Hidalgo**](https://www.gineshidalgo.com), [**Zhe Cao**](https://people.eecs.berkeley.edu/~zhecao), [**Tomas Simon**](http://www.cs.cmu.edu/~tsimon), [**Shih-En Wei**](https://scholar.google.com/citations?user=sFQD3k4AAAAJ&hl=en), [**Yaadhav Raaj**](https://www.raaj.tech), [**Hanbyul Joo**](https://jhugestar.github.io), **and** [**Yaser Sheikh**](http://www.cs.cmu.edu/~yaser). It is **maintained by** [**Ginés Hidalgo**](https://www.gineshidalgo.com) **and** [**Yaadhav Raaj**](https://www.raaj.tech). OpenPose would not be possible without the [**CMU Panoptic Studio dataset**](http://domedb.perception.cs.cmu.edu). We would also like to thank all the people who [has helped OpenPose in any way](doc/09_authors_and_contributors.md).
Authors Ginés Hidalgo (left) and Hanbyul Joo (right) in front of the CMU Panoptic Studio
## Contents
1. [Results](#results)
2. [Features](#features)
3. [Related Work](#related-work)
4. [Installation](#installation)
5. [Quick Start Overview](#quick-start-overview)
6. [Send Us Feedback!](#send-us-feedback)
7. [Citation](#citation)
8. [License](#license)
## Results
### Whole-body (Body, Foot, Face, and Hands) 2D Pose Estimation
Testing OpenPose: (Left) Crazy Uptown Funk flashmob in Sydney video sequence. (Center and right) Authors Ginés Hidalgo and Tomas Simon testing face and hands
### Whole-body 3D Pose Reconstruction and Estimation
Tianyi Zhao testing the OpenPose 3D Module
### Unity Plugin
Tianyi Zhao and Ginés Hidalgo testing the OpenPose Unity Plugin
### Runtime Analysis
We show an inference time comparison between the 3 available pose estimation libraries (same hardware and conditions): OpenPose, Alpha-Pose (fast Pytorch version), and Mask R-CNN. The OpenPose runtime is constant, while the runtime of Alpha-Pose and Mask R-CNN grow linearly with the number of people. More details [**here**](https://arxiv.org/abs/1812.08008).
## Features
**Main Functionality**:
- **2D real-time multi-person keypoint detection**:
- 15, 18 or **25-keypoint body/foot keypoint estimation**, including **6 foot keypoints**. **Runtime invariant to number of detected people**.
- **2x21-keypoint hand keypoint estimation**. **Runtime depends on number of detected people**. See [**OpenPose Training**](https://github.com/CMU-Perceptual-Computing-Lab/openpose_train) for a runtime invariant alternative.
- **70-keypoint face keypoint estimation**. **Runtime depends on number of detected people**. See [**OpenPose Training**](https://github.com/CMU-Perceptual-Computing-Lab/openpose_train) for a runtime invariant alternative.
- [**3D real-time single-person keypoint detection**](doc/advanced/3d_reconstruction_module.md):
- 3D triangulation from multiple single views.
- Synchronization of Flir cameras handled.
- Compatible with Flir/Point Grey cameras.
- [**Calibration toolbox**](doc/advanced/calibration_module.md): Estimation of distortion, intrinsic, and extrinsic camera parameters.
- **Single-person tracking** for further speedup or visual smoothing.
**Input**: Image, video, webcam, Flir/Point Grey, IP camera, and support to add your own custom input source (e.g., depth camera).
**Output**: Basic image + keypoint display/saving (PNG, JPG, AVI, ...), keypoint saving (JSON, XML, YML, ...), keypoints as array class, and support to add your own custom output code (e.g., some fancy UI).
**OS**: Ubuntu (20, 18, 16, 14), Windows (10, 8), Mac OSX, Nvidia TX2.
**Hardware compatibility**: CUDA (Nvidia GPU), OpenCL (AMD GPU), and non-GPU (CPU-only) versions.
**Usage Alternatives**:
- [**Command-line demo**](doc/01_demo.md) for built-in functionality.
- [**C++ API**](doc/04_cpp_api.md/) and [**Python API**](doc/03_python_api.md) for custom functionality. E.g., adding your custom inputs, pre-processing, post-posprocessing, and output steps.
For further details, check the [major released features](doc/07_major_released_features.md) and [release notes](doc/08_release_notes.md) docs.
## Related Work
- [**OpenPose training code**](https://github.com/CMU-Perceptual-Computing-Lab/openpose_train)
- [**OpenPose foot dataset**](https://cmu-perceptual-computing-lab.github.io/foot_keypoint_dataset/)
- [**OpenPose Unity Plugin**](https://github.com/CMU-Perceptual-Computing-Lab/openpose_unity_plugin)
- OpenPose papers published in **IEEE TPAMI and CVPR**. Cite them in your publications if OpenPose helps your research! (Links and more details in the [Citation](#citation) section below).
## Installation
If you want to use OpenPose without installing or writing any code, simply [download and use the latest Windows portable version of OpenPose](doc/installation/0_index.md#windows-portable-demo)!
Otherwise, you could [build OpenPose from source](doc/installation/0_index.md#compiling-and-running-openpose-from-source). See the [installation doc](doc/installation/0_index.md) for all the alternatives.
## Quick Start Overview
Simply use the OpenPose Demo from your favorite command-line tool (e.g., Windows PowerShell or Ubuntu Terminal). E.g., this example runs OpenPose on your webcam and displays the body keypoints:
```
# Ubuntu
./build/examples/openpose/openpose.bin
```
```
:: Windows - Portable Demo
bin\OpenPoseDemo.exe --video examples\media\video.avi
```
You can also add any of the available flags in any order. E.g., the following example runs on a video (`--video {PATH}`), enables face (`--face`) and hands (`--hand`), and saves the output keypoints on JSON files on disk (`--write_json {PATH}`).
```
# Ubuntu
./build/examples/openpose/openpose.bin --video examples/media/video.avi --face --hand --write_json output_json_folder/
```
```
:: Windows - Portable Demo
bin\OpenPoseDemo.exe --video examples\media\video.avi --face --hand --write_json output_json_folder/
```
Optionally, you can also extend OpenPose's functionality from its Python and C++ APIs. After [installing](doc/installation/0_index.md) OpenPose, check its [official doc](doc/00_index.md) for a quick overview of all the alternatives and tutorials.
## Send Us Feedback!
Our library is open source for research purposes, and we want to improve it! So let us know (create a new GitHub issue or pull request, email us, etc.) if you...
1. Find/fix any bug (in functionality or speed) or know how to speed up or improve any part of OpenPose.
2. Want to add/show some cool functionality/demo/project made on top of OpenPose. We can add your project link to our [Community-based Projects](doc/10_community_projects.md) section or even integrate it with OpenPose!
## Citation
Please cite these papers in your publications if OpenPose helps your research. All of OpenPose is based on [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://arxiv.org/abs/1812.08008), while the hand and face detectors also use [Hand Keypoint Detection in Single Images using Multiview Bootstrapping](https://arxiv.org/abs/1704.07809) (the face detector was trained using the same procedure than the hand detector).
@article{8765346,
author = {Z. {Cao} and G. {Hidalgo Martinez} and T. {Simon} and S. {Wei} and Y. A. {Sheikh}},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
title = {OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields},
year = {2019}
}
@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}
}
Paper links:
- OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields:
- [IEEE TPAMI](https://ieeexplore.ieee.org/document/8765346)
- [ArXiv](https://arxiv.org/abs/1812.08008)
- [Hand Keypoint Detection in Single Images using Multiview Bootstrapping](https://arxiv.org/abs/1704.07809)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://arxiv.org/abs/1611.08050)
- [Convolutional Pose Machines](https://arxiv.org/abs/1602.00134)
## License
OpenPose is freely available for free non-commercial use, and may be redistributed under these conditions. Please, see the [license](./LICENSE) for further details. Interested in a commercial license? Check this [FlintBox link](https://cmu.flintbox.com/#technologies/b820c21d-8443-4aa2-a49f-8919d93a8740). For commercial queries, use the `Contact` section from the [FlintBox link](https://cmu.flintbox.com/#technologies/b820c21d-8443-4aa2-a49f-8919d93a8740) and also send a copy of that message to [Yaser Sheikh](mailto:yaser@cs.cmu.edu).
