ScanGAN360: 面向360°图像的真实扫描路径生成模型
ScanGAN360: A Generative Model of Realistic Scanpaths for 360$^{\circ}$ Images
AI总结：
针对现有方法难以生成360°图像真实扫描路径的问题，提出ScanGAN360生成对抗模型，采用球面动态时间规整损失与新型扫描路径参数化，生成质量大幅优于现有方法、接近人类水平，可用于注视行为研究与虚拟场景设计。
摘要：
理解并建模360°环境下人类注视行为的动态特性是计算机视觉与虚拟现实领域的核心挑战。生成对抗方法可通过为未见过的图像生成大量可能的扫描路径，缓解这一难题。然而，现有扫描路径生成方法无法充分预测360°图像的真实扫描路径。我们提出ScanGAN360——一种解决这一挑战性问题的新型生成对抗方法。我们的网络生成器针对代表沉浸式环境的360°图像的特性进行了定制。具体而言，我们通过采用球面适配的动态时间规整作为损失函数，并提出一种新颖的360°扫描路径参数化方法来实现这一目标。我们生成的扫描路径质量大幅优于对比方法，几乎与人类基线水平相当。因此，ScanGAN360能够快速模拟大量行为与真实用户相仿的虚拟观察者，助力更好地理解注视行为，并为虚拟场景设计提供新的应用可能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
