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How the Presence and Size of Static Peripheral Blur Affects Cybersickness in Virtual Reality
ACM Transactions on Applied Perception ( IF 1.9 ) Pub Date : 2020-11-06 , DOI: 10.1145/3419984
Yun-Xuan Lin, Rohith Venkatakrishnan, Roshan Venkatakrishnan, Elham Ebrahimi, Wen-Chieh Lin, Sabarish V. Babu

Cybersickness (CS) is one of the challenges that has hindered the widespread adoption of Virtual Reality and its applications. Consequently, a number of studies have focused on extensively understanding and reducing CS. Inspired by previous work that has sought to reduce CS using foveated rendering and Field of View (FOV) restrictions, we investigated how the presence and size of a static central window in peripheral FOV blurring affects CS. To facilitate this peripheral FOV blur, we applied a Gaussian blur effect in the display peripheral region, provisioning a full-resolution central window. Thirty participants took part in a three-session, within-subjects experiment, performing search and spatial updating tasks in a first-person, slow-walking, maze-traveling scenario. Two different central window sizes (small and large) were tested against a baseline condition that didn’t feature display peripheral blurring. Results revealed that the baseline condition produced higher levels of CS than both conditions with a central window. While there were no significant differences between the small and large windows, we observed interaction effects suggesting an influence of window size on “adaptation to CS.” When the central window is small, adaptation to CS seems to take more time but is more pronounced. The interventions had no effect on spatial updating and presence, but were detectable when the blurred area was larger (small central window). Lower sickness levels observed in both window conditions supports the use of peripheral FOV blurring to reduce CS, reducing our dependence on eye tracking. This being said, researchers must strive to find the right balance between window size and detectability to ensure seamless virtual experiences.

中文翻译:

静态周边模糊的存在和大小如何影响虚拟现实中的网络病

Cyber​​sickness (CS) 是阻碍虚拟现实及其应用广泛采用的挑战之一。因此,许多研究都集中在广泛理解和减少 CS 上。受先前试图使用中心点渲染和视野 (FOV) 限制来减少 CS 的工作的启发,我们研究了外围 FOV 模糊中静态中心窗口的存在和大小如何影响 CS。为了促进这种外围 FOV 模糊,我们在显示外围区域应用了高斯模糊效果,提供了一个全分辨率的中央窗口。30 名参与者参加了一个三期的受试者内实验,在第一人称、慢行、迷宫旅行的场景中执行搜索和空间更新任务。针对没有显示周边模糊的基线条件测试了两种不同的中央窗口大小(小和大)。结果显示,基线条件产生的 CS 水平高于具有中央窗口的两种条件。虽然小窗口和大窗口之间没有显着差异,但我们观察到交互效应表明窗口大小对“适应 CS”有影响。当中央窗口较小时,对 CS 的适应似乎需要更多时间,但更为明显。干预对空间更新和存在没有影响,但当模糊区域较大(小中央窗口)时可以检测到。在两种窗口条件下观察到的较低疾病水平支持使用外围 FOV 模糊来减少 CS,从而减少我们对眼动追踪的依赖。话虽如此,
更新日期:2020-11-06
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