| Autor: |
Martin, Daniel, Serrano, Ana, Bergman, Alexander W., Wetzstein, Gordon, Masia, Belen |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
IEEE Transactions on Visualization and Computer Graphics 2022 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/TVCG.2022.3150502 |
| Popis: |
Understanding and modeling the dynamics of human gaze behavior in 360$^\circ$ environments is a key challenge in computer vision and virtual reality. Generative adversarial approaches could alleviate this challenge by generating a large number of possible scanpaths for unseen images. Existing methods for scanpath generation, however, do not adequately predict realistic scanpaths for 360$^\circ$ images. We present ScanGAN360, a new generative adversarial approach to address this challenging problem. Our network generator is tailored to the specifics of 360$^\circ$ images representing immersive environments. Specifically, we accomplish this by leveraging the use of a spherical adaptation of dynamic-time warping as a loss function and proposing a novel parameterization of 360$^\circ$ scanpaths. The quality of our scanpaths outperforms competing approaches by a large margin and is almost on par with the human baseline. ScanGAN360 thus allows fast simulation of large numbers of virtual observers, whose behavior mimics real users, enabling a better understanding of gaze behavior and novel applications in virtual scene design. |
| Databáze: |
arXiv |
| Externí odkaz: |
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