CMU-Perceptual-Computing-Lab/openpose

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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, Zhe Cao, Tomas Simon, Shih-En Wei, Yaadhav Raaj, Hanbyul Joo, and Yaser Sheikh. It is maintained by Ginés Hidalgo and Yaadhav Raaj. OpenPose would not be possible without the CMU Panoptic Studio dataset. We would also like to thank all the people who have helped OpenPose in any way.

^{Authors Ginés Hidalgo (left) and Hanbyul Joo (right) in front of the CMU Panoptic Studio}

1. Results
2. Features
3. Related Work
4. Installation
5. Quick Start Overview
6. Send Us Feedback!
7. Citation
8. License

^{Testing OpenPose: (Left) Crazy Uptown Funk flashmob in Sydney video sequence. (Center and right) Authors Ginés Hidalgo and Tomas Simon testing face and hands}

^{Tianyi Zhao testing the OpenPose 3D Module}

^{Tianyi Zhao and Ginés Hidalgo testing the OpenPose Unity Plugin}

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.

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 for a runtime invariant alternative.
  • 70-keypoint face keypoint estimation. Runtime depends on number of detected people