  This paper explores the capabilities of convolutional neural networks to deal
with a task that is easily manageable for humans: perceiving 3D pose of a human
body from varying angles. However, in our approach, we are restricted to using
a monocular vision system. For this purpose, we apply a convolutional neural
network approach on RGB videos and extend it to three dimensional convolutions.
This is done via encoding the time dimension in videos as the 3\ts{rd}
dimension in convolutional space, and directly regressing to human body joint
positions in 3D coordinate space. This research shows the ability of such a
network to achieve state-of-the-art performance on the selected Human3.6M
dataset, thus demonstrating the possibility of successfully representing
temporal data with an additional dimension in the convolutional operation.
