Automatic camera control aims to define a framework to control virtual camera movements in dynamic and unpredictable virtual environments while ensuring a set of desired visual properties. We investigate the relationship between camera placement and playing behaviour in games and build a user model of the camera behaviour that can be used to control camera movements based on player preferences. For this purpose, we collect eye gaze, camera and game-play data from subjects playing a 3D platform game, we cluster gaze and camera information to identify camera behaviour profiles and we employ machine learning to build predictive models of the virtual camera behaviour. The performance of the models on unseen data reveals accuracies above 70% for all the player behaviour types identified. The characteristics of the generated models, their limits and their use for creating adaptive automatic camera control in games is discussed.
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