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 (by FORTH-ModelBasedTracker)
Review mocap bvh-format bvh neural-network Tensorflow Computer Vision Ensemble 2d-to-3d Demo 3d-animation gesture-recognition rgb-images Webcam pose-estimation 3d-pose-estimation Real-time
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892
3.3
7 days ago

FORTH-ModelBasedTracker/MocapNET is an open source project licensed under GNU General Public License v3.0 or later which is an OSI approved license.

The primary programming language of MocapNET is C++.

Popular Comparisons

  1. MocapNET VS openpose
  2. MocapNET VS gestures2anything
  3. MocapNET VS 3DObjectTracking
  4. MocapNET VS webcamoid
  5. MocapNET VS Bullet-Physics-Demo
  6. MocapNET VS freemocap-uwupose
  7. MocapNET VS PoseEstimationForMobile
  8. MocapNET VS rerun-animation
  9. MocapNET VS ncnn
  10. MocapNET VS tensorflow

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