Shypd.aiThe largest open index of AI toolsMocapNET is a real-time method that estimates 3D human pose directly in the Bio Vision Hierarchy (BVH) format from monocular color images. It offers high accuracy and real-time performance, even with occlusions.
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MocapNET is a real-time method for estimating 3D human pose, converting 2D body joint estimations from monocular color images directly into the popular Bio Vision Hierarchy (BVH) format. Its key contributions include a novel and compact 2D pose NSRM representation, a human body orientation classifier, and an ensemble of orientation-tuned neural networks. This allows for the decomposition of the body into upper and lower kinematic hierarchies, enabling robust pose recovery even with significant occlusions. An efficient Inverse Kinematics solver refines the neural-network-based solution, ensuring 3D human pose estimations are consistent with a target person's limb sizes. MocapNET achieves a 33% accuracy improvement over its predecessor while maintaining real-time performance of 70 fps on CPU-only execution.
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