  Eye-tracking technology is an integral component of new display devices such
as virtual and augmented reality headsets. Applications of gaze information
range from new interaction techniques exploiting eye patterns to
gaze-contingent digital content creation. However, system latency is still a
significant issue in many of these applications because it breaks the
synchronization between the current and measured gaze positions. Consequently,
it may lead to unwanted visual artifacts and degradation of user experience. In
this work, we focus on foveated rendering applications where the quality of an
image is reduced towards the periphery for computational savings. In foveated
rendering, the presence of latency leads to delayed updates to the rendered
frame, making the quality degradation visible to the user. To address this
issue and to combat system latency, recent work proposes to use saccade landing
position prediction to extrapolate the gaze information from delayed
eye-tracking samples. While the benefits of such a strategy have already been
demonstrated, the solutions range from simple and efficient ones, which make
several assumptions about the saccadic eye movements, to more complex and
costly ones, which use machine learning techniques. Yet, it is unclear to what
extent the prediction can benefit from accounting for additional factors. This
paper presents a series of experiments investigating the importance of
different factors for saccades prediction in common virtual and augmented
reality applications. In particular, we investigate the effects of saccade
orientation in 3D space and smooth pursuit eye-motion (SPEM) and how their
influence compares to the variability across users. We also present a simple
yet efficient correction method that adapts the existing saccade prediction
methods to handle these factors without performing extensive data collection.
