[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-1751":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":25,"hasPages":25,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":46,"readmeContent":47,"aiSummary":48,"trendingCount":16,"starSnapshotCount":16,"syncStatus":49,"lastSyncTime":50,"discoverSource":51},1751,"openpose","CMU-Perceptual-Computing-Lab\u002Fopenpose","CMU-Perceptual-Computing-Lab","OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation","https:\u002F\u002Fcmu-perceptual-computing-lab.github.io\u002Fopenpose",null,"C++",34147,8046,902,340,0,3,20,73,17,45,"Other",false,"master",true,[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,5,43,44,45],"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","pose","pose-estimation","real-time","2026-06-12 02:00:32","\u003Cdiv align=\"center\">\n    \u003Cimg src=\".github\u002FLogo_main_black.png\" width=\"300\">\n\u003C\u002Fdiv>\n\n-----------------\n\n| **Build Type**   |`Linux`           |`MacOS`           |`Windows`         |\n| :---:            | :---:            | :---:            | :---:            |\n| **Build Status** | [![Status](https:\u002F\u002Fgithub.com\u002FCMU-Perceptual-Computing-Lab\u002Fopenpose\u002Fworkflows\u002FCI\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002FCMU-Perceptual-Computing-Lab\u002Fopenpose\u002Factions) | [![Status](https:\u002F\u002Fgithub.com\u002FCMU-Perceptual-Computing-Lab\u002Fopenpose\u002Fworkflows\u002FCI\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002FCMU-Perceptual-Computing-Lab\u002Fopenpose\u002Factions) | [![Status](https:\u002F\u002Fci.appveyor.com\u002Fapi\u002Fprojects\u002Fstatus\u002F5leescxxdwen77kg\u002Fbranch\u002Fmaster?svg=true)](https:\u002F\u002Fci.appveyor.com\u002Fproject\u002Fgineshidalgo99\u002Fopenpose\u002Fbranch\u002Fmaster) |\n\n[**OpenPose**](https:\u002F\u002Fgithub.com\u002FCMU-Perceptual-Computing-Lab\u002Fopenpose) 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**.\n\nIt is **authored by** [**Ginés Hidalgo**](https:\u002F\u002Fwww.gineshidalgo.com), [**Zhe Cao**](https:\u002F\u002Fpeople.eecs.berkeley.edu\u002F~zhecao), [**Tomas Simon**](http:\u002F\u002Fwww.cs.cmu.edu\u002F~tsimon), [**Shih-En Wei**](https:\u002F\u002Fscholar.google.com\u002Fcitations?user=sFQD3k4AAAAJ&hl=en), [**Yaadhav Raaj**](https:\u002F\u002Fwww.raaj.tech), [**Hanbyul Joo**](https:\u002F\u002Fjhugestar.github.io), **and** [**Yaser Sheikh**](http:\u002F\u002Fwww.cs.cmu.edu\u002F~yaser). It is **maintained by** [**Ginés Hidalgo**](https:\u002F\u002Fwww.gineshidalgo.com) **and** [**Yaadhav Raaj**](https:\u002F\u002Fwww.raaj.tech). OpenPose would not be possible without the [**CMU Panoptic Studio dataset**](http:\u002F\u002Fdomedb.perception.cs.cmu.edu). We would also like to thank all the people who [have helped OpenPose in any way](doc\u002F09_authors_and_contributors.md).\n\n\n\n\u003Cp align=\"center\">\n    \u003Cimg src=\".github\u002Fmedia\u002Fpose_face_hands.gif\" width=\"480\">\n    \u003Cbr>\n    \u003Csup>Authors \u003Ca href=\"https:\u002F\u002Fwww.gineshidalgo.com\" target=\"_blank\">Ginés Hidalgo\u003C\u002Fa> (left) and \u003Ca href=\"https:\u002F\u002Fjhugestar.github.io\" target=\"_blank\">Hanbyul Joo\u003C\u002Fa> (right) in front of the \u003Ca href=\"http:\u002F\u002Fdomedb.perception.cs.cmu.edu\" target=\"_blank\">CMU Panoptic Studio\u003C\u002Fa>\u003C\u002Fsup>\n\u003C\u002Fp>\n\n\n\n## Contents\n1. [Results](#results)\n2. [Features](#features)\n3. [Related Work](#related-work)\n4. [Installation](#installation)\n5. [Quick Start Overview](#quick-start-overview)\n6. [Send Us Feedback!](#send-us-feedback)\n7. [Citation](#citation)\n8. [License](#license)\n\n\n\n## Results\n### Whole-body (Body, Foot, Face, and Hands) 2D Pose Estimation\n\u003Cp align=\"center\">\n    \u003Cimg src=\".github\u002Fmedia\u002Fdance_foot.gif\" width=\"300\">\n    \u003Cimg src=\".github\u002Fmedia\u002Fpose_face.gif\" width=\"300\">\n    \u003Cimg src=\".github\u002Fmedia\u002Fpose_hands.gif\" width=\"300\">\n    \u003Cbr>\n    \u003Csup>Testing OpenPose: (Left) \u003Ca href=\"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=2DiQUX11YaY\" target=\"_blank\">\u003Ci>Crazy Uptown Funk flashmob in Sydney\u003C\u002Fi>\u003C\u002Fa> video sequence. (Center and right) Authors \u003Ca href=\"https:\u002F\u002Fwww.gineshidalgo.com\" target=\"_blank\">Ginés Hidalgo\u003C\u002Fa> and \u003Ca href=\"http:\u002F\u002Fwww.cs.cmu.edu\u002F~tsimon\" target=\"_blank\">Tomas Simon\u003C\u002Fa> testing face and hands\u003C\u002Fsup>\n\u003C\u002Fp>\n\n### Whole-body 3D Pose Reconstruction and Estimation\n\u003Cp align=\"center\">\n    \u003Cimg src=\".github\u002Fmedia\u002Fopenpose3d.gif\" width=\"360\">\n    \u003Cbr>\n    \u003Csup>\u003Ca href=\"https:\u002F\u002Fziutinyat.github.io\u002F\" target=\"_blank\">Tianyi Zhao\u003C\u002Fa> testing the OpenPose 3D Module\u003C\u002Fa>\u003C\u002Fsup>\n\u003C\u002Fp>\n\n### Unity Plugin\n\u003Cp align=\"center\">\n    \u003Cimg src=\".github\u002Fmedia\u002Funity_main.png\" width=\"300\">\n    \u003Cimg src=\".github\u002Fmedia\u002Funity_body_foot.png\" width=\"300\">\n    \u003Cimg src=\".github\u002Fmedia\u002Funity_hand_face.png\" width=\"300\">\n    \u003Cbr>\n    \u003Csup>\u003Ca href=\"https:\u002F\u002Fziutinyat.github.io\u002F\" target=\"_blank\">Tianyi Zhao\u003C\u002Fa> and \u003Ca href=\"https:\u002F\u002Fwww.gineshidalgo.com\" target=\"_blank\">Ginés Hidalgo\u003C\u002Fa> testing the \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FCMU-Perceptual-Computing-Lab\u002Fopenpose_unity_plugin\" target=\"_blank\">OpenPose Unity Plugin\u003C\u002Fa>\u003C\u002Fsup>\n\u003C\u002Fp>\n\n### Runtime Analysis\nWe 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:\u002F\u002Farxiv.org\u002Fabs\u002F1812.08008).\n\n\u003Cp align=\"center\">\n    \u003Cimg src=\".github\u002Fmedia\u002Fopenpose_vs_competition.png\" width=\"360\">\n\u003C\u002Fp>\n\n\n\n## Features\n**Main Functionality**:\n- **2D real-time multi-person keypoint detection**:\n    - 15, 18 or **25-keypoint body\u002Ffoot keypoint estimation**, including **6 foot keypoints**. **Runtime invariant to number of detected people**.\n    - **2x21-keypoint hand keypoint estimation**. **Runtime depends on number of detected people**. See [**OpenPose Training**](https:\u002F\u002Fgithub.com\u002FCMU-Perceptual-Computing-Lab\u002Fopenpose_train) for a runtime invariant alternative.\n    - **70-keypoint face keypoint estimation**. **Runtime depends on number of detected people**. See [**OpenPose Training**](https:\u002F\u002Fgithub.com\u002FCMU-Perceptual-Computing-Lab\u002Fopenpose_train) for a runtime invariant alternative.\n- [**3D real-time single-person keypoint detection**](doc\u002Fadvanced\u002F3d_reconstruction_module.md):\n    - 3D triangulation from multiple single views.\n    - Synchronization of Flir cameras handled.\n    - Compatible with Flir\u002FPoint Grey cameras.\n- [**Calibration toolbox**](doc\u002Fadvanced\u002Fcalibration_module.md): Estimation of distortion, intrinsic, and extrinsic camera parameters.\n- **Single-person tracking** for further speedup or visual smoothing.\n\n**Input**: Image, video, webcam, Flir\u002FPoint Grey, IP camera, and support to add your own custom input source (e.g., depth camera).\n\n**Output**: Basic image + keypoint display\u002Fsaving (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).\n\n**OS**: Ubuntu (20, 18, 16, 14), Windows (10, 8), Mac OSX, Nvidia TX2.\n\n**Hardware compatibility**: CUDA (Nvidia GPU), OpenCL (AMD GPU), and non-GPU (CPU-only) versions.\n\n**Usage Alternatives**:\n- [**Command-line demo**](doc\u002F01_demo.md) for built-in functionality.\n- [**C++ API**](doc\u002F04_cpp_api.md\u002F) and [**Python API**](doc\u002F03_python_api.md) for custom functionality. E.g., adding your custom inputs, pre-processing, post-posprocessing, and output steps.\n\nFor further details, check the [major released features](doc\u002F07_major_released_features.md) and [release notes](doc\u002F08_release_notes.md) docs.\n\n\n\n## Related Work\n- [**OpenPose training code**](https:\u002F\u002Fgithub.com\u002FCMU-Perceptual-Computing-Lab\u002Fopenpose_train)\n- [**OpenPose foot dataset**](https:\u002F\u002Fcmu-perceptual-computing-lab.github.io\u002Ffoot_keypoint_dataset\u002F)\n- [**OpenPose Unity Plugin**](https:\u002F\u002Fgithub.com\u002FCMU-Perceptual-Computing-Lab\u002Fopenpose_unity_plugin)\n- 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).\n\n\n\n## Installation\nIf you want to use OpenPose without installing or writing any code, simply [download and use the latest Windows portable version of OpenPose](doc\u002Finstallation\u002F0_index.md#windows-portable-demo)!\n\nOtherwise, you could [build OpenPose from source](doc\u002Finstallation\u002F0_index.md#compiling-and-running-openpose-from-source). See the [installation doc](doc\u002Finstallation\u002F0_index.md) for all the alternatives.\n\n\n\n## Quick Start Overview\nSimply 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:\n```\n# Ubuntu\n.\u002Fbuild\u002Fexamples\u002Fopenpose\u002Fopenpose.bin\n```\n```\n:: Windows - Portable Demo\nbin\\OpenPoseDemo.exe --video examples\\media\\video.avi\n```\n\nYou 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}`).\n```\n# Ubuntu\n.\u002Fbuild\u002Fexamples\u002Fopenpose\u002Fopenpose.bin --video examples\u002Fmedia\u002Fvideo.avi --face --hand --write_json output_json_folder\u002F\n```\n```\n:: Windows - Portable Demo\nbin\\OpenPoseDemo.exe --video examples\\media\\video.avi --face --hand --write_json output_json_folder\u002F\n```\n\nOptionally, you can also extend OpenPose's functionality from its Python and C++ APIs. After [installing](doc\u002Finstallation\u002F0_index.md) OpenPose, check its [official doc](doc\u002F00_index.md) for a quick overview of all the alternatives and tutorials.\n\n\n\n## Send Us Feedback!\nOur 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...\n1. Find\u002Ffix any bug (in functionality or speed) or know how to speed up or improve any part of OpenPose.\n2. Want to add\u002Fshow some cool functionality\u002Fdemo\u002Fproject made on top of OpenPose. We can add your project link to our [Community-based Projects](doc\u002F10_community_projects.md) section or even integrate it with OpenPose!\n\n\n\n## Citation\nPlease 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:\u002F\u002Farxiv.org\u002Fabs\u002F1812.08008), while the hand and face detectors also use [Hand Keypoint Detection in Single Images using Multiview Bootstrapping](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.07809) (the face detector was trained using the same procedure as the hand detector).\n\n    @article{8765346,\n      author = {Z. {Cao} and G. {Hidalgo Martinez} and T. {Simon} and S. {Wei} and Y. A. {Sheikh}},\n      journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},\n      title = {OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields},\n      year = {2019}\n    }\n\n    @inproceedings{simon2017hand,\n      author = {Tomas Simon and Hanbyul Joo and Iain Matthews and Yaser Sheikh},\n      booktitle = {CVPR},\n      title = {Hand Keypoint Detection in Single Images using Multiview Bootstrapping},\n      year = {2017}\n    }\n\n    @inproceedings{cao2017realtime,\n      author = {Zhe Cao and Tomas Simon and Shih-En Wei and Yaser Sheikh},\n      booktitle = {CVPR},\n      title = {Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields},\n      year = {2017}\n    }\n\n    @inproceedings{wei2016cpm,\n      author = {Shih-En Wei and Varun Ramakrishna and Takeo Kanade and Yaser Sheikh},\n      booktitle = {CVPR},\n      title = {Convolutional pose machines},\n      year = {2016}\n    }\n\nPaper links:\n- OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields:\n    - [IEEE TPAMI](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8765346)\n    - [ArXiv](https:\u002F\u002Farxiv.org\u002Fabs\u002F1812.08008)\n- [Hand Keypoint Detection in Single Images using Multiview Bootstrapping](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.07809)\n- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.08050)\n- [Convolutional Pose Machines](https:\u002F\u002Farxiv.org\u002Fabs\u002F1602.00134)\n\n\n\n## License\nOpenPose is freely available for free non-commercial use, and may be redistributed under these conditions. Please, see the [license](.\u002FLICENSE) for further details. Interested in a commercial license? Check this [FlintBox link](https:\u002F\u002Fcmu.flintbox.com\u002F#technologies\u002Fb820c21d-8443-4aa2-a49f-8919d93a8740). For commercial queries, use the `Contact` section from the [FlintBox link](https:\u002F\u002Fcmu.flintbox.com\u002F#technologies\u002Fb820c21d-8443-4aa2-a49f-8919d93a8740) and also send a copy of that message to [Yaser Sheikh](mailto:yaser@cs.cmu.edu).\n","OpenPose 是一个用于实时多人关键点检测的库，能够同时估计人体、面部、手部和脚部的关键点。该项目采用C++编写，利用深度学习技术实现了对单张图像中多达135个关键点的精确识别。其核心技术基于Caffe框架，并结合了计算机视觉领域的多项先进算法，如多任务卷积神经网络等。OpenPose 支持跨平台部署（Linux, MacOS, Windows），并提供了易于使用的API接口。该工具非常适合应用于需要对人体姿态进行分析的场景，例如动作捕捉、人机交互系统开发以及体育训练辅助等领域。",2,"2026-06-11 02:45:48","top_all"]