# OpenPose
**Wikidata**: [Q109595481](https://www.wikidata.org/wiki/Q109595481)  
**Source**: https://4ort.xyz/entity/openpose

## Summary
OpenPose is real-time multi-person 2D pose estimation software developed by CMU Perceptual Computing Lab. It uses part affinity fields to detect human body poses in images and videos.

## Key Facts
- OpenPose is a software implementation for detecting human body poses in real-time
- The project is developed by the CMU Perceptual Computing Lab at Carnegie Mellon University
- The source code is hosted on GitHub at https://github.com/CMU-Perceptual-Computing-Lab/openpose
- The software is written in Python
- OpenPose has multiple stable versions including 1.0.0 (October 31, 2017) through 1.5.1 (September 4, 2019)
- The project has documentation available at https://cmu-perceptual-computing-lab.github.io/openpose/
- OpenPose is documented in the paper "Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields"
- The software has Japanese Wikipedia coverage

## FAQs
### Q: What is OpenPose used for?
A: OpenPose is used for detecting and tracking human body poses in real-time from images and videos. It identifies key body points like joints and limbs across multiple people simultaneously.

### Q: How accurate is OpenPose compared to other pose estimation tools?
A: The specific accuracy metrics aren't provided in the source material, but OpenPose is notable for its real-time performance and ability to handle multiple people simultaneously using part affinity fields.

### Q: Can OpenPose be used in commercial applications?
A: The source material doesn't specify licensing information, so details about commercial usage would need to be checked in the official documentation or repository.

## Why It Matters
OpenPose represents a significant advancement in computer vision, particularly for human pose estimation. Before its introduction, many pose estimation methods struggled with real-time performance and handling multiple people in the same frame. OpenPose's part affinity fields approach allowed for more accurate and efficient detection of body keypoints, making it valuable for applications in human-computer interaction, action recognition, animation, healthcare monitoring, and sports analysis. Its open-source nature has democratized access to advanced pose estimation technology, enabling researchers and developers worldwide to build upon its foundation.

## Notable For
- Real-time multi-person pose estimation using part affinity fields
- Being developed by CMU Perceptual Computing Lab, a leading research institution in computer vision
- Open-source availability on GitHub since 2017
- Consistent release cycle with multiple stable versions through 2019
- Implementation in Python, making it accessible to a broader developer community

## Body
### Overview
OpenPose is a software framework for real-time human pose estimation that can detect multiple people simultaneously. It is developed by the CMU Perceptual Computing Lab at Carnegie Mellon University.

### Technical Implementation
- Written in Python
- Uses part affinity fields for pose estimation
- Source code repository: https://github.com/CMU-Perceptual-Computing-Lab/openpose
- Documentation available at: https://cmu-perceptual-computing-lab.github.io/openpose/

### Version History
OpenPose has undergone multiple stable releases since its initial version:

- Version 1.0.0 - Released October 31, 2017
- Version 1.0.1 - Released October 31, 2017
- Version 1.0.2 - Released October 31, 2017
- Version 1.1.0 - Released October 31, 2017
- Version 1.2.0 - Released November 3, 2017
- Version 1.2.1 - Released January 9, 2018
- Version 1.3.0 - Released March 24, 2018
- Version 1.4.0 - Released September 2, 2018
- Version 1.5.0 - Released May 16, 2019
- Version 1.5.1 - Released September 4, 2019

### Research Foundation
The software is based on the research paper titled "OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields," which introduced the part affinity fields approach to pose estimation. This method represents a significant contribution to the field of computer vision and human pose detection.

### Availability
- Open-source project on GitHub
- Available in Japanese on Wikipedia
- Documentation and research papers accessible through the project website and repository

### Current Status
As of the provided information, the latest stable version is 1.5.1, released in September 2019. The project appears to be actively maintained based on the consistent release history.

## References

1. [2025](https://github.com/EvanLi/Github-Ranking/blob/master/Data/github-ranking-2025-07-06.csv)
2. [Release 1.0.0. 2017](https://github.com/CMU-Perceptual-Computing-Lab/openpose/releases/tag/v1.0.0)
3. [Release 1.0.1. 2017](https://github.com/CMU-Perceptual-Computing-Lab/openpose/releases/tag/v1.0.1)
4. [Release 1.0.2. 2017](https://github.com/CMU-Perceptual-Computing-Lab/openpose/releases/tag/v1.0.2)
5. [Release 1.1.0. 2017](https://github.com/CMU-Perceptual-Computing-Lab/openpose/releases/tag/v1.1.0)
6. [Release 1.2.0. 2017](https://github.com/CMU-Perceptual-Computing-Lab/openpose/releases/tag/v1.2.0)
7. [Release 1.2.1. 2018](https://github.com/CMU-Perceptual-Computing-Lab/openpose/releases/tag/v1.2.1)
8. [Release 1.3.0. 2018](https://github.com/CMU-Perceptual-Computing-Lab/openpose/releases/tag/v1.3.0)
9. [Release 1.4.0. 2018](https://github.com/CMU-Perceptual-Computing-Lab/openpose/releases/tag/v1.4.0)
10. [Release 1.5.0. 2019](https://github.com/CMU-Perceptual-Computing-Lab/openpose/releases/tag/v1.5.0)
11. [Release 1.5.1. 2019](https://github.com/CMU-Perceptual-Computing-Lab/openpose/releases/tag/v1.5.1)
12. [Release 1.6.0. 2020](https://github.com/CMU-Perceptual-Computing-Lab/openpose/releases/tag/v1.6.0)
13. [Release 1.7.0. 2020](https://github.com/CMU-Perceptual-Computing-Lab/openpose/releases/tag/v1.7.0)
14. [Source](https://api.github.com/repos/CMU-Perceptual-Computing-Lab/openpose)
15. [Source](https://github.com/CMU-Perceptual-Computing-Lab/openpose)