  We propose a view-invariant method towards the assessment of the quality of
human movements which does not rely on skeleton data. Our end-to-end
convolutional neural network consists of two stages, where at first a
view-invariant trajectory descriptor for each body joint is generated from RGB
images, and then the collection of trajectories for all joints are processed by
an adapted, pre-trained 2D CNN (e.g. VGG-19 or ResNeXt-50) to learn the
relationship amongst the different body parts and deliver a score for the
movement quality. We release the only publicly-available, multi-view,
non-skeleton, non-mocap, rehabilitation movement dataset (QMAR), and provide
results for both cross-subject and cross-view scenarios on this dataset. We
show that VI-Net achieves average rank correlation of 0.66 on cross-subject and
0.65 on unseen views when trained on only two views. We also evaluate the
proposed method on the single-view rehabilitation dataset KIMORE and obtain
0.66 rank correlation against a baseline of 0.62.
