CMU-Perceptual-Computing-Lab/openpose

OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation

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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. 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: Input: Image, video, webcam, Flir/Point Grey, IP camera, and support to add your own custom input source (e.g., depth camera).…

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What this repository does

OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation

Repository facts

Owner: CMU-Perceptual-Computing-Lab
Primary language: C++
Stars: 34,547
Forks: 8,041
Open issues + pull requests: 360
License: Other
Archived: No
Default branch: master
Created: 2017-04-24T14:06:31.000Z
Last push: 2024-08-03T01:59:11.000Z

Topics and intended use

Owner-supplied topics: caffe, computer-vision, cpp, cvpr-2017, deep-learning, face, foot-estimation, hand-estimation, human-behavior-understanding, human-pose, human-pose-estimation, keypoint-detection, keypoints, machine-learning, multi-person, opencv, openpose, pose, pose-estimation, real-time

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