Volumetric video is an emerging key technology for immersive representation
of 3D spaces and objects. Rendering volumetric video requires lots of
computational power which is challenging especially for mobile devices. To
mitigate this, we developed a streaming system that renders a 2D view from the
volumetric video at a cloud server and streams a 2D video stream to the client.
However, such network-based processing increases the motion-to-photon (M2P)
latency due to the additional network and processing delays. In order to
compensate the added latency, prediction of the future user pose is necessary.
We developed a head motion prediction model and investigated its potential to
reduce the M2P latency for different look-ahead times. Our results show that
the presented model reduces the rendering errors caused by the M2P latency
compared to a baseline system in which no prediction is performed.Comment: 7 pages, 4 figure