#OpenPose

OpenPose detects 135 keypoints for body, hands, face, and feet in real-time across multiple subjects in both 2D and 3D. The system supports diverse input sources such as images, videos, and webcams, and offers consistent runtime performance. It is compatible with Ubuntu, Windows, and macOS and utilizes CUDA and OpenCL for improved performance. OpenPose can be integrated via C++ and Python APIs, offering flexibility for input and output processes, suitable for computer vision and AI research.

This project features a bottom-up approach to real-time multi-person pose estimation, removing the need for person detectors. It achieved recognition in the 2016 MSCOCO Keypoints Challenge and the ECCV Best Demo Award. The approach is implemented across various platforms including C++, TensorFlow, and PyTorch, providing flexible options for developers. The Python code aligns with the latest MSCOCO models and suits diverse system inputs from images to webcams, leveraging deep learning for enhanced human pose recognition.

The project enhances the OpenPose algorithm for effective 2D multi-person pose estimation on CPU, maintaining accuracy. It employs a streamlined model to identify 18 keypoints per individual, achieving 40% AP on the COCO 2017 validation set. The repository includes essential code for training, validation, and conversion to OpenVINO format, with C++ and Python demo support, catering to developers seeking efficient pose estimation in limited-resource settings.

The Monocular Total Capture project offers tools for reconstructing 3D models of human facial and bodily features from single-camera video inputs in real-world settings. By deploying the Adam model, which bases its structures on SMPL and FaceWarehouse sources, this project ensures precise pose estimations. Installation involves Ubuntu, OpenPose, and compatible software, with the entire setup downloadable via GitHub. Addition of Docker technology facilitates efficient deployment and testing with sample videos. This project remains exclusive to non-commercial research, furnishing a foundation for advancing studies in comprehensive 3D human modeling.