Introducing OpenPose
OpenPose is a groundbreaking open-source project that offers a real-time multi-person system capable of detecting human keypoints, including body, hands, face, and feet, on single images. It identifies a total of 135 keypoints and remains a pioneering tool in the field of computer vision and human pose estimation.
Authors and Contributors
The project is developed by a team of talented individuals, including Ginés Hidalgo, Zhe Cao, Tomas Simon, Shih-En Wei, Yaadhav Raaj, Hanbyul Joo, and Yaser Sheikh. Maintenance and ongoing development are also managed by Ginés Hidalgo and Yaadhav Raaj, with invaluable support from the CMU Panoptic Studio dataset and numerous contributors.
Key Features
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2D Real-time Multi-person Keypoint Detection: OpenPose can estimate body, foot, hand, and face keypoints from images in real time. This includes 15 to 25 body and foot keypoints, 42 hand keypoints across both hands, and 70 facial keypoints.
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3D Keypoint Detection: The system can perform 3D keypoint estimation for a single person, utilizing triangulation techniques from multiple camera views, supporting Flir and Point Grey cameras.
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Input and Output Versatility: OpenPose supports various input types such as images, videos, and data from webcams or IP cameras. It can produce outputs as images, keypoint data in JSON or XML formats, and integrates easily into various types of user interfaces.
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Platform Compatibility: The software runs on multiple operating systems, including Ubuntu, Windows, Mac OSX, and Nvidia TX2. It is compatible with GPUs via CUDA (Nvidia) or OpenCL (AMD), as well as non-GPU setups.
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Integration Options: There are command-line demos available, as well as C++ and Python APIs for users who wish to customize or extend the software functionalities.
Related Work and Extensions
OpenPose is supported by several related projects and resources such as a dedicated foot keypoint dataset, a Unity Plugin for integration with Unity development environments, and published academic papers showcased in IEEE TPAMI and CVPR publications. These resources enhance the functionality and applicability of OpenPose, making it useful for researchers and developers alike.
Installation and Quick Start
For users who want to try OpenPose without much setup, there is a Windows portable version available. More technical users can build OpenPose from source on various platforms. Quick start guides allow users to run the software easily on webcams or videos, with the ability to enable specific keypoint detections (e.g., face, hands) and save outputs to disk.
Engaging with the Community
As an open-source project aimed initially at research purposes, the OpenPose team encourages feedback from the community. Users can report bugs, suggest improvements, or highlight projects and demos built on top of OpenPose. The community aspect ensures continuous evolution and improvement of the project.
Citation Guidelines
OpenPose has made significant contributions to the field of human pose estimation. It is built upon academic research, and users and researchers are encouraged to cite the relevant papers in their publications if they use OpenPose for their research endeavors.
License Information
OpenPose is free for non-commercial use, provided under specific conditions outlined in its license. There are options for commercial licensing through designated channels for those interested in integrating OpenPose into commercial projects.
OpenPose represents a substantial leap in real-time pose estimation technology, leveraging collaborative efforts to remain at the forefront of innovation in computational image processing.