PoseEstimationForMobile
tensorflow_Realtime_Multi-Person_Pose_Estimation
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PoseEstimationForMobile | tensorflow_Realtime_Multi-Person_Pose_Estimation | |
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1 | 1 | |
991 | 212 | |
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0.0 | 0.0 | |
about 1 year ago | over 1 year ago | |
C++ | Jupyter Notebook | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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PoseEstimationForMobile
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Is a refresh coming?
Tonal has a pretty beefy android system in it. https://github.com/edvardHua/PoseEstimationForMobile
tensorflow_Realtime_Multi-Person_Pose_Estimation
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[Help] Multiple Keypoints Estimation
I've seen this kind of repo too : https://github.com/michalfaber/tensorflow_Realtime_Multi-Person_Pose_Estimation
What are some alternatives?
MocapNET - We present MocapNET, a real-time method that estimates the 3D human pose directly in the popular Bio Vision Hierarchy (BVH) format, given estimations of the 2D body joints originating from monocular color images. Our contributions include: (a) A novel and compact 2D pose NSRM representation. (b) A human body orientation classifier and an ensemble of orientation-tuned neural networks that regress the 3D human pose by also allowing for the decomposition of the body to an upper and lower kinematic hierarchy. This permits the recovery of the human pose even in the case of significant occlusions. (c) An efficient Inverse Kinematics solver that refines the neural-network-based solution providing 3D human pose estimations that are consistent with the limb sizes of a target person (if known). All the above yield a 33% accuracy improvement on the Human 3.6 Million (H3.6M) dataset compared to the baseline method (MocapNET) while maintaining real-time performance
openpifpaf - Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association" in PyTorch.
Cocos2d - Cocos2d-x is a suite of open-source, cross-platform, game-development tools utilized by millions of developers across the globe. Its core has evolved to serve as the foundation for Cocos Creator 1.x & 2.x.
LoFTR - Code for "LoFTR: Detector-Free Local Feature Matching with Transformers", CVPR 2021, T-PAMI 2022