This paper introduces a novel visual attention model to compute user's gaze position automatically, i.e. without using a gazetracking system. Our model is specifically designed for real-time first-person exploration of 3D virtual environments. It is the first model adapted to this context which can compute, in real-time, a continuous gaze point position instead of a set of 3D objects potentially observed by the user. To do so, contrary to previous models which use a mesh-based representation of visual objects, we introduce a representation based on surface-elements. Our model also simulates visual reflexes and the cognitive process which takes place in the brain such as the gaze behavior associated to firstperson navigation in the virtual environment. Our visual attention model combines the bottom-up and top-down components to compute a continuous gaze point position on screen that hopefully matches the user's one. We have conducted an experiment to study and compare the performance of our method with a state-of-the-art approach. Our results are found significantly better with more than 100% of accuracy gained. This suggests that computing in realtime a gaze point in a 3D virtual environment is possible and is a valid approach as compared to object-based approaches.
Task-relevant gaze behavior associated to first-person navigation in a VE has been estimated by combining bottom-up and top-down components to compute user gaze point position on screen REF .
A real-time visual attention model for predicting gaze point during first-person exploration of virtual environments
VRST '10
Computer Science