  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 intrinsic view-invariant properties of human
pose between simultaneous frames from different viewpoints and their
equivariant properties between augmented frames from the same viewpoint. We
evaluate the learned view-invariant pose representations for two downstream
tasks. We perform comparative experiments that show improvements on the
state-of-the-art unsupervised cross-view action classification accuracy on NTU
RGB+D by a significant margin, on both RGB and depth images. We also show the
efficiency of transferring the learned representations from NTU RGB+D to obtain
the first ever unsupervised cross-view and cross-subject rank correlation
results on the multi-view human movement quality dataset, QMAR, and marginally
improve on the-state-of-the-art supervised results for this dataset. We also
carry out ablation studies to examine the contributions of the different
components of our proposed network.
