Popular GitHub Repositories for Openpose

pytorch implementation of openpose including Hand and Body Pose Estimation.

Fast and accurate human pose estimation in PyTorch. Contains implementation of "Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose" paper.

Library for Fast and Flexible Human Pose Estimation

:basketball::robot::basketball: AI web app and API to analyze basketball shots and shooting pose.

online 3d openpose editor for stable diffusion and controlnet

Perform Human Pose Estimation in OpenCV Using OpenPose MobileNet

AI 尬舞机

tf-openpose Based iOS Project

This is a pix2pix demo that learns from pose and translates this into a human. A webcam-enabled application is also provided that translates your pose to the trained pose. Everybody dance now !

A collection of ControlNet poses

Openpose implementation using darknet

Detects the sitting position of a person

The baseline project for inferencing various Pose Estimation tflite models with TFLiteSwift on iOS

PyTorch implementation of the OpenPose

Qualia is a deep learning framework deeply integrated with automatic differentiation and dynamic graphing with CUDA acceleration. Qualia was built from scratch.

A markerless, low monetary cost, accessible approach to human gait analysis using an OpenPose-based 2D estimation system for knee flexion landmarks. Clinical gait angles, points of contact, and errors in estimation plotted.

Stable diffusion random controlnet pose generator

Real time Human Pose estimation using Tensorflow and OpenCV

This includes a novel method to measure the quality of the actions performed in Olympic weightlifting using human action recognition in videos. Human action recognition is a well-studied problem in computer vision and on the other hand action quality assessment is researched and experimented comparatively low. This is due to the lack of datasets that can be used to assess the quality of actions. In this research, we introduce a method to assess player techniques in weightlifting by using skeleton-based human action recognition. Furthermore, we introduce a new video dataset for action recognition in weightlifting which is annotated to frame level. We intended to develop a viable automated scoring system through action recognition that would be beneficial in the sports industry.