A Gaze Prediction Model for Task-Oriented Virtual Reality

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dc.contributor.authorMammou, Konstantinaen_US
dc.contributor.authorMania, Katerinaen_US
dc.contributor.editorGünther, Tobiasen_US
dc.contributor.editorMontazeri, Zahraen_US
dc.date.accessioned2025-05-09T09:31:15Z
dc.date.available2025-05-09T09:31:15Z
dc.date.issued2025
dc.description.abstractIn this work, we present a gaze prediction model for Virtual Reality task-oriented environments. Unlike past work which focuses on gaze prediction for specific tasks, we investigate the role and potential of temporal continuity in enabling accurate predictions in diverse task categories. The model reduces input complexity while maintaining high prediction accuracy. Evaluated on the OpenNEEDS dataset, it significantly outperforms baseline methods. The model demonstrates strong potential for integration into gaze-based VR interactions and foveated rendering pipelines. Future work will focus on runtime optimization and expanding evaluation across diverse VR scenarios.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationEurographics 2025 - Posters
dc.identifier.doi10.2312/egp.20251020
dc.identifier.isbn978-3-03868-269-1
dc.identifier.issn1017-4656
dc.identifier.pages2 pages
dc.identifier.urihttps://doi.org/10.2312/egp.20251020
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/egp20251020
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing → Virtual reality; Computing methodologies → Neural networks; Rendering
dc.subjectHuman centered computing → Virtual reality
dc.subjectComputing methodologies → Neural networks
dc.subjectRendering
dc.titleA Gaze Prediction Model for Task-Oriented Virtual Realityen_US
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