117683 20240319081002.0 doi 10.1109/TVCG.2022.3150502 sideral 128178 ART-2022-128178 eng Martin Serrano, D. Universidad de Zaragoza (orcid)0000-0002-0073-6398 ScanGAN360: a generative model of realistic scanpaths for 360 images 2022 Access copy available to the general public Unrestricted Understanding and modeling the dynamics of human gaze behavior in 360° environments is crucial for creating, improving, and developing emerging virtual reality applications. However, recruiting human observers and acquiring enough data to analyze their behavior when exploring virtual environments requires complex hardware and software setups, and can be time-consuming. Being able to generate virtual observers can help overcome this limitation, and thus stands as an open problem in this medium. Particularly, generative adversarial approaches could alleviate this challenge by generating a large number of scanpaths that reproduce human behavior when observing new scenes, essentially mimicking virtual observers. However, existing methods for scanpath generation do not adequately predict realistic scanpaths for 360° images. We present ScanGAN360, a new generative adversarial approach to address this problem. We propose a novel loss function based on dynamic time warping and tailor our network to the specifics of 360° images. The quality of our generated scanpaths outperforms competing approaches by a large margin, and is almost on par with the human baseline. ScanGAN360 allows fast simulation of large numbers of virtual observers, whose behavior mimics real users, enabling a better understanding of gaze behavior, facilitating experimentation, and aiding novel applications in virtual reality and beyond. 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/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 5.2 2022 COMPUTER SCIENCE, SOFTWARE ENGINEERING 15 / 108 = 0.139 2022 Q1 T1 1.515 2022 Computer Graphics and Computer-Aided Design 2022 Q1 Software 2022 Q1 Signal Processing 2022 Q1 Computer Vision and Pattern Recognition 2022 Q1 10.5 2022 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Serrano Pacheu, A. Universidad de Zaragoza (orcid)0000-0002-7796-3177 Bergman, A. W. Wetzstein, G. Masia Corcoy, B. Universidad de Zaragoza (orcid)0000-0003-0060-7278 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 28, 5 (2022), 2003-2013 IEEE trans. vis. comput. graph. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 1077-2626 18425512 http://zaguan.unizar.es/record/117683/files/texto_completo.pdf Postprint 3357956 http://zaguan.unizar.es/record/117683/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:117683 articulos driver 2024-03-18-14:14:38 ARTICLE