129356 20241125101145.0 doi 10.1109/TVCG.2023.3320237 sideral 135293 ART-2023-135293 eng Bernal-Berdún, Edurne Universidad de Zaragoza (orcid)0000-0002-5275-8652 D-SAV360: A Dataset of Gaze Scanpaths on 360° Ambisonic Videos 2023 Access copy available to the general public Unrestricted Understanding human visual behavior within virtual reality environments is crucial to fully leverage their potential. While previous research has provided rich visual data from human observers, existing gaze datasets often suffer from the absence of multimodal stimuli. Moreover, no dataset has yet gathered eye gaze trajectories (i.e., scanpaths) for dynamic content with directional ambisonic sound, which is a critical aspect of sound perception by humans. To address this gap, we introduce D-SAV360, a dataset of 4,609 head and eye scanpaths for 360° videos with first-order ambisonics. This dataset enables a more comprehensive study of multimodal interaction on visual behavior in virtual reality environments. We analyze our collected scanpaths from a total of 87 participants viewing 85 different videos and show that various factors such as viewing mode, content type, and gender significantly impact eye movement statistics. We demonstrate the potential of D-SAV360 as a benchmarking resource for state-of-the-art attention prediction models and discuss its possible applications in further research. By providing a comprehensive dataset of eye movement data for dynamic, multimodal virtual environments, our work can facilitate future investigations of visual behavior and attention in virtual reality. info:eu-repo/grantAgreement/ES/AEI/PID2019-105004GB-I00 info:eu-repo/grantAgreement/ES/AEI/PID2022-141539NB-I00 info:eu-repo/grantAgreement/EC/H2020/682080/EU/Intuitive editing of visual appearance from real-world datasets/CHAMELEON This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 682080-CHAMELEON info:eu-repo/grantAgreement/EC/H2020/956585/EU/Predictive Rendering In Manufacture and Engineering/PRIME This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 956585-PRIME info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 4.7 2023 COMPUTER SCIENCE, SOFTWARE ENGINEERING 14 / 132 = 0.106 2023 Q1 T1 2.056 2023 Computer Graphics and Computer-Aided Design 2023 Q1 Software 2023 Q1 Signal Processing 2023 Q1 Computer Vision and Pattern Recognition 2023 Q1 10.4 2023 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Martín, Daniel Universidad de Zaragoza (orcid)0000-0002-0073-6398 Malpica, Sandra Universidad de Zaragoza (orcid)0000-0002-8016-7649 Pérez, Pedro J. Gutierrez, Diego Universidad de Zaragoza (orcid)0000-0002-7503-7022 Masia, Belén Universidad de Zaragoza (orcid)0000-0003-0060-7278 Serrano, Ana Universidad de Zaragoza (orcid)0000-0002-7796-3177 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 29, 11 (2023), 4350-4360 IEEE trans. vis. comput. graph. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 1077-2626 1034669 http://zaguan.unizar.es/record/129356/files/texto_completo.pdf Postprint 2952032 http://zaguan.unizar.es/record/129356/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:129356 articulos driver 2024-11-22-12:04:17 ARTICLE