OpenPose is a real-time system for multi-person 2D pose estimation, including body, foot, hand, and facial keypoints. The method uses Part Affinity Fields (PAFs) to associate body parts with individuals in an image. PAFs encode the location and orientation of limbs, enabling efficient and accurate pose estimation. The system is trained on a large annotated foot dataset, allowing for the detection of both body and foot keypoints. The method outperforms previous approaches in terms of both accuracy and runtime performance. OpenPose is an open-source library that provides a robust solution for real-time multi-person pose estimation, with applications in computer vision and machine learning. The system is designed to handle a wide range of scenarios, including crowded scenes, occlusions, and varying scales. The method is evaluated on several benchmarks, including the MPII and COCO datasets, demonstrating its effectiveness in detecting human poses. The system is also compared to other state-of-the-art methods, showing its computational efficiency and accuracy. OpenPose is capable of running on various platforms, including GPUs, CPUs, and embedded systems, making it a versatile tool for real-time pose estimation. The system's performance is further enhanced by the use of PAFs, which provide a more accurate representation of body parts and their relationships. The method is also able to handle complex scenarios, such as vehicle keypoint estimation, demonstrating its generalizability. Overall, OpenPose represents a significant advancement in real-time multi-person pose estimation, offering a fast and accurate solution for a wide range of applications.OpenPose is a real-time system for multi-person 2D pose estimation, including body, foot, hand, and facial keypoints. The method uses Part Affinity Fields (PAFs) to associate body parts with individuals in an image. PAFs encode the location and orientation of limbs, enabling efficient and accurate pose estimation. The system is trained on a large annotated foot dataset, allowing for the detection of both body and foot keypoints. The method outperforms previous approaches in terms of both accuracy and runtime performance. OpenPose is an open-source library that provides a robust solution for real-time multi-person pose estimation, with applications in computer vision and machine learning. The system is designed to handle a wide range of scenarios, including crowded scenes, occlusions, and varying scales. The method is evaluated on several benchmarks, including the MPII and COCO datasets, demonstrating its effectiveness in detecting human poses. The system is also compared to other state-of-the-art methods, showing its computational efficiency and accuracy. OpenPose is capable of running on various platforms, including GPUs, CPUs, and embedded systems, making it a versatile tool for real-time pose estimation. The system's performance is further enhanced by the use of PAFs, which provide a more accurate representation of body parts and their relationships. The method is also able to handle complex scenarios, such as vehicle keypoint estimation, demonstrating its generalizability. Overall, OpenPose represents a significant advancement in real-time multi-person pose estimation, offering a fast and accurate solution for a wide range of applications.