Gaze prediction using machine learning for dynamic stereo manipulation in games.

Comfortable, high-quality 3D stereo viewing is becoming a requirement for interactive applications today. Previous research shows that manipulating disparity can alleviate some of the discomfort caused by 3D stereo, but it is best to do this locally, around the object the user is gazing at. The main challenge is thus to develop a gaze predictor in the demanding context of real-time, heavily task-oriented applications such as games. Our key observation is that player actions are highly correlated with the present state of a game, encoded by game variables. Based on this, we train a classifier to learn these correlations using an eye-tracker which provides the ground-truth object being looked at. The classifier is used at runtime to predict object category - and thus gaze - during game play, based on the current state of game variables. We use this prediction to propose a dynamic disparity manipulation method, which provides rich and comfortable depth. We evaluate the quality of our gaze predictor numerically and experimentally, showing that it predicts gaze more accurately than previous approaches. A subjective rating study demonstrates that our localized disparity manipulation is preferred over previous methods

CollabAR - Investigating the Mediating Role of Mobile AR Interfaces on Co-Located Group Collaboration

Mobile Augmented Reality (AR) technology is enabling new applications for different domains including architecture, education or medical work. As AR interfaces project digital data, information and models into the real world, it allows for new forms of collaborative work. However, despite the wide availability of AR applications, very little is known about how AR interfaces mediate and shape collaborative practices. This paper presents a study which examines how a mobile AR (M-AR) interface for inspecting and discovering AR models of varying complexity impacts co-located group practices. We contribute new insights into how current mobile AR interfaces impact co-located collaboration. Our results show that M-AR interfaces induce high mental load and frustration, cause a high number of context switches between devices and group discussion, and overall leads to a reduction in group interaction. We present design recommendations for future work focusing on collaborative AR interfaces