D-SAV360: A Dataset of Gaze Scanpaths on 360° Ambisonic Videos

Bernal-Berdún, Edurne (Universidad de Zaragoza) ; Martín, Daniel (Universidad de Zaragoza) ; Malpica, Sandra (Universidad de Zaragoza) ; Pérez, Pedro J. ; Gutierrez, Diego (Universidad de Zaragoza) ; Masia, Belén (Universidad de Zaragoza) ; Serrano, Ana (Universidad de Zaragoza)

Resumen: 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.

Idioma: Inglés

DOI: 10.1109/TVCG.2023.3320237

Año: 2023

Publicado en: IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 29, 11 (2023), 4350-4360
ISSN: 1077-2626

Factor impacto JCR: 4.7 (2023)

Categ. JCR: COMPUTER SCIENCE, SOFTWARE ENGINEERING rank: 14 / 131 = 0.107 (2023) - Q1 - T1

Factor impacto SCIMAGO: 2.056 - Computer Graphics and Computer-Aided Design (Q1) - Software (Q1) - Signal Processing (Q1) - Computer Vision and Pattern Recognition (Q1)

Financiación: info:eu-repo/grantAgreement/ES/AEI/PID2019-105004GB-I00
Financiación: info:eu-repo/grantAgreement/ES/AEI/PID2022-141539NB-I00
Financiación: info:eu-repo/grantAgreement/EC/H2020/682080/EU/Intuitive editing of visual appearance from real-world datasets/CHAMELEON
Financiación: info:eu-repo/grantAgreement/EC/H2020/956585/EU/Predictive Rendering In Manufacture and Engineering/PRIME

Tipo y forma: Article (PostPrint)

Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)