  The acceptance of immersive telepresence systems is impeded by the latency
that is present when mediating the realistic feeling of presence in a remote
environment to a local human user. A disagreement between the user's ego-motion
and the visual response provokes the emergence of motion sickness. Viewport or
head motion (HM) prediction techniques play a key role in compensating the
noticeable delay between the user and the remote site. We present a deep
learning-based viewport prediction paradigm that fuses past HM trajectories
with scene semantics in a late-fusion manner. Real HM profiles are used to
evaluate the proposed approach. A mean compensation rate as high as 99.99% is
obtained, clearly outperforming the state-of-the-art. An on-demand 360-degree
video streaming framework is presented to prove its general validity. The
proposed approach increases the perceived video quality while requiring a
significantly lower transmission rate.
