arXiv:2507.11075v2 Announce Type: replace-cross Abstract: Marker-free human pose estimation (HPE) has found increasing applications in various fields. Current HPE suffers from occasional errors in keypoint recognition and random fluctuation in keypoint trajectories when analyzing kinematic human poses. The performance of existing deep learning-based models for HPE refinement is considerably limited by inaccurate training datasets in which the keypoints are manually annotated. This paper proposed a novel method to overcome the difficulty, in which the key techniques include: (i) A robust joint
This research addresses a critical limitation in current marker-free human pose estimation technologies, which are becoming increasingly vital for various computational applications.
Improved accuracy in kinematic human pose estimation can unlock more reliable and sophisticated interactions between AI systems and the physical world, impacting fields from robotics to virtual reality.
The ability to refine HPE using robust joint angle learning bypasses the need for manually annotated datasets, potentially accelerating the development and deployment of more accurate and dynamic AI systems.
- · Robotics industry
- · Computer Vision researchers
- · Gaming and AR/VR developers
- · Medical diagnostics
- · Companies relying on manual pose annotation
- · Less robust pose estimation methods
More accurate and reliable human motion capture becomes possible without specialized hardware.
This improved accuracy can lead to more sophisticated human-robot interaction and realistic character animation in media.
Long-term, this could enable new forms of physical rehabilitation, athletic training, and general-purpose humanoid robot deployment.
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Read at arXiv cs.AI