Abstract
There is increased interest in using generative AI to create 3D spaces for Virtual Reality (VR) applications. However, today's models produce artificial environments, falling short of supporting collaborative tasks that benefit from incorporating the user's physical context. To generate environments that support VR telepresence, we introduce SpaceBlender, a novel pipeline that utilizes generative AI techniques to blend users' physical surroundings into unified virtual spaces. This pipeline transforms user-provided 2D images into context-rich 3D environments through an iterative process consisting of depth estimation, mesh alignment, and diffusion-based space completion guided by geometric priors and adaptive text prompts. In a preliminary within-subjects study, where 20 participants performed a collaborative VR affinity diagramming task in pairs, we compared SpaceBlender with a generic virtual environment and a state-of-the-art scene generation framework, evaluating its ability to create virtual spaces suitable for collaboration. Participants appreciated the enhanced familiarity and context provided by SpaceBlender but also noted complexities in the generative environments that could detract from task focus. Drawing on participant feedback, we propose directions for improving the pipeline and discuss the value and design of blended spaces for different scenarios.
Abstract (translated)
越来越多的人对使用生成式AI创建虚拟现实（VR）应用中的3D空间感兴趣。然而，目前的模型产生的假定环境不足以支持用户物理上下文中的协作任务。为了生成支持VR远程存在的环境，我们引入了SpaceBlender，一种利用生成式AI技术将用户周围物理环境混合到统一虚拟空间的新管道。这个管道通过深度估计、网格对齐和扩散引导的迭代过程，将用户提供的2D图像转换为上下文丰富的3D环境。在初步的自我对照研究中，20名参与者在一对中执行了协同VR偏好图任务，我们将SpaceBlender与通用虚拟环境和最先进的场景生成框架进行了比较，以评估其创建适合协作的虚拟空间的能力。参与者赞赏SpaceBlender提供的增强熟悉度和上下文，但也指出在生成环境中存在复杂性，可能会分散任务关注力。根据参与者的反馈，我们提出了改进管道的方向，并讨论了不同场景下混合空间的值和设计。
URL
https://arxiv.org/abs/2409.13926
