FORTH Computational Vision and Robotics Laboratory (@FORTH-ModelBasedTracker)
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FORTH-ModelBasedTracker

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1

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
C++
803
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2

HandTracker

3D Hand Tracking using input from a depth sensor.
Python
311
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3

PyOpenPose

Python bindings for the Openpose library
Jupyter Notebook
285
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4

MonocularRGB_3D_Handpose_WACV18

Using a single RGB frame for real time 3D hand pose estimation in the wild
Python
160
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5

MonocularRGB_2D_Handjoints_MVA19

Accurate Hand Keypoint Localization on Mobile Devices
Python
65
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6

reading_group

Reading group material and links
7
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7

mocapnet_rosnode

A ROS node for the MocapNET 3D Pose Estimator
C++
5
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8

wacv_docker

A Dockerfile for our WACV18 paper: Using a single RGB frame for real time 3D hand pose estimation in the wild.
Dockerfile
1
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