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The acceptance of immersive telepresence systems is impeded by the latencythat is present when mediating the realistic feeling of presence in a remoteenvironment to a local human user. A disagreement between the user's ego-motionand the visual response provokes the emergence of motion sickness. Viewport orhead motion (HM) prediction techniques play a key role in compensating thenoticeable delay between the user and the remote site. We present a deeplearning-based viewport prediction paradigm that fuses past HM trajectorieswith scene semantics in a late-fusion manner. Real HM profiles are used toevaluate the proposed approach. A mean compensation rate as high as 99.99% isobtained, clearly outperforming the state-of-the-art. An on-demand 360-degreevideo streaming framework is presented to prove its general validity. Theproposed approach increases the perceived video quality while requiring asignificantly lower transmission rate.

HSMF-Net: Semantic Viewport Prediction for Immersive Telepresence and On-Demand 360-degree Video