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• W3138572511 abstract "Human visual attention in immersive virtual reality (VR) is key for many important applications, such as content design, gaze-contingent rendering, or gaze-based interaction. However, prior works typically focused on free-viewing conditions that have limited relevance for practical applications. We first collect eye tracking data of 27 participants performing a visual search task in four immersive VR environments. Based on this dataset, we provide a comprehensive analysis of the collected data and reveal correlations between users' eye fixations and other factors, i.e. users' historical gaze positions, task-related objects, saliency information of the VR content, and users' head rotation velocities. Based on this analysis, we propose FixationNet - a novel learning-based model to forecast users' eye fixations in the near future in VR. We evaluate the performance of our model for free-viewing and task-oriented settings and show that it outperforms the state of the art by a large margin of 19.8% (from a mean error of 2.93° to 2.35°) in free-viewing and of 15.1% (from 2.05° to 1.74°) in task-oriented situations. As such, our work provides new insights into task-oriented attention in virtual environments and guides future work on this important topic in VR research." @default.
• W3138572511 created "2021-03-29" @default.
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• W3138572511 date "2021-05-01" @default.
• W3138572511 modified "2023-10-16" @default.
• W3138572511 title "FixationNet: Forecasting Eye Fixations in Task-Oriented Virtual Environments" @default.
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• W3138572511 doi "https://doi.org/10.1109/tvcg.2021.3067779" @default.
• W3138572511 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33750707" @default.
• W3138572511 hasPublicationYear "2021" @default.
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