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Unsupervised View-Invariant Human Posture Representation
[article]
2021
arXiv
pre-print
Most recent view-invariant action recognition and performance assessment approaches rely on a large amount of annotated 3D skeleton data to extract view-invariant features. However, acquiring 3D skeleton data can be cumbersome, if not impractical, in in-the-wild scenarios. To overcome this problem, we present a novel unsupervised approach that learns to extract view-invariant 3D human pose representation from a 2D image without using 3D joint data. Our model is trained by exploiting the
arXiv:2109.08730v1
fatcat:7bq3hfft4vd4ngtt6qkbgjiwtq