Pose estimation


 * https://github.com/ZheC/Realtime_Multi-Person_Pose_Estimation OpenPose is freely available for free non-commercial use, and may be redistributed under these conditions. Please, see the license for further details. Interested in a commercial license? Check this FlintBox link. For commercial queries, use the Directly Contact Organization section from the FlintBox link and also send a copy of that message to Yaser Sheikh.

This is why https://aws.amazon.com/rekognition/ is expensive, they have to pay lots of money for using open source proprietary code. In South africa you do whatever you want, if you have assets, then setup a Fronting company: your BEE empowerment partner can't go to jail if he loses the court case.

https://github.com/CMU-Perceptual-Computing-Lab/openpose used by priya dwivedi

https://arxiv.org/abs/1611.08050

densepose
http://densepose.org/

https://www.youtube.com/watch?v=EMjPqgLX14A Can machine vision map humans from videos to 3D Models? Yes! DensePose is a new architecture by the team at Facebook AI research that does just that. It uses a convolutional network with some special features like region of interest pooling and cascading to make this happen. It was also trained on a newly created labeled dataset that mapped human poses to 3D models. The team open sourced the dataset but not the code, but using the details in the paper we can recreate their results. I'll explain how it works in this video. https://github.com/llSourcell/3D_Pose_Estimation

https://github.com/n1ckfg/OpenPoseRig

https://github.com/facebookresearch/VideoPose3D

https://github.com/facebookresearch/pythia

nvidia labs
https://github.com/NVlabs/Deep_Object_Pose This is the official DOPE ROS package for detection and 6-DoF pose estimation of known objects from an RGB camera. The network has been trained on the following YCB objects: cracker box, sugar box, tomato soup can, mustard bottle, potted meat can, and gelatin box. For more details, see our CoRL 2018 paper and video.
 * https://www.youtube.com/watch?v=yVGViBqWtBI&feature=youtu.be For the first time, an algorithm trained only on synthetic data is able to beat a state-of-the-art network trained on real images for object pose estimation on several objects of a standard benchmark. Learn more here: https://nvda.ws/2CvO2Jy

https://arxiv.org/abs/1809.10790 Using synthetic data for training deep neural networks for robotic manipulation holds the promise of an almost unlimited amount of pre-labeled training data, generated safely out of harm's way. One of the key challenges of synthetic data, to date, has been to bridge the so-called reality gap, so that networks trained on synthetic data operate correctly when exposed to real-world data. We explore the reality gap in the context of 6-DoF pose estimation of known objects from a single RGB image. We show that for this problem the reality gap can be successfully spanned by a simple combination of domain randomized and photorealistic data. Using synthetic data generated in this manner, we introduce a one-shot deep neural network that is able to perform competitively against a state-of-the-art network trained on a combination of real and synthetic data. To our knowledge, this is the first deep network trained only on synthetic data that is able to achieve state-of-the-art performance on 6-DoF object pose estimation. Our network also generalizes better to novel environments including extreme lighting conditions, for which we show qualitative results. Using this network we demonstrate a real-time system estimating object poses with sufficient accuracy for real-world semantic grasping of known household objects in clutter by a real robot.

timctho
https://github.com/timctho/VNect-tensorflow

http://gvv.mpi-inf.mpg.de/projects/VNect/

https://github.com/timctho/convolutional-pose-machines-tensorflow

links
Opencv

Yolo