Fast and accurate human pose estimation in PyTorch. Contains implementation of "Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose" paper.
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Updated
Apr 30, 2024 - Python
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
🏀🤖🏀 AI web app and API to analyze basketball shots and shooting pose.
Apply ML to the skeletons from OpenPose; 9 actions; multiple people. (WARNING: I'm sorry that this is only good for course demo, not for real world applications !!! Those ary very difficult !!!)
Everybody dance now reproduced in pytorch
Training repository for OpenPose
Perform Human Pose Estimation in OpenCV Using OpenPose MobileNet
Markerless kinematics with any cameras — From 2D Pose estimation to 3D OpenSim motion
RTMPose series (RTMPose, DWPose, RTMO, RTMW) without mmcv, mmpose, mmdet etc.
Ultra-lightweight human body posture key point CNN model. ModelSize:2.3MB HUAWEI P40 NCNN benchmark: 6ms/img,
Implement of Openpose use Tensorflow
A tensorflow implementation of Arxiv Paper "Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose "(https://arxiv.org/abs/1811.12004)
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 !
Behavioral segmentation of open field in DeepLabCut, or B-SOID ("B-side"), is a pipeline that pairs unsupervised pattern recognition with supervised classification to achieve fast predictions of behaviors that are not predefined by users.
Detects the sitting position of a person
Code for estimating social distances from RGB cameras.
Posture recognition based on common camera
Human Image Gender Classifier for Expressive Body Capture
🏀 Judging basketball shots and analyzing shooting pose with machine learning
GUI based on the python api of openpose in windows using cuda10 and cudnn7. Support body , hand, face keypoints estimation and data saving. Realtime gesture recognition is realized through two-layer neural network based on the skeleton collected from the gui.
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