As virtual 3D environments become prevalent, equitable access is crucial for blind and low-vision (BLV) users who face challenges with spatial awareness, navigation, and interactions. To address this gap, previous work explored supplementing visual information with auditory and haptic modalities. However, these methods are static and offer limited support for dynamic, in-context adaptation. Recent work in generative AI enables users to query and modify 3D scenes via natural language, introducing a paradigm with increased flexibility and control for accessibility improvements. We present RAVEN, a system that responds to query or modification prompts from BLV users to improve the runtime accessibility of 3D virtual scenes. We evaluated the system with eight BLV people, uncovering key insights into the strengths and shortcomings of generative AI-driven accessibility in virtual 3D environments, pointing to promising results as well as challenges related to system reliability and user trust.

翻译：随着虚拟三维环境的日益普及，保障盲人及低视力用户在空间感知、导航与交互方面面临挑战时的公平访问至关重要。为弥补这一差距，先前研究探索了通过听觉与触觉模态补充视觉信息的方法。然而，这些方法具有静态特性，对动态、情境自适应的支持有限。生成式人工智能的最新进展使用户能够通过自然语言查询与修改三维场景，为提升可访问性引入了更具灵活性与可控性的新范式。本文提出RAVEN系统，该系统通过响应盲人及低视力用户的查询或修改指令，以提升三维虚拟场景的运行时可访问性。我们邀请八位盲人及低视力参与者对系统进行评估，揭示了生成式人工智能驱动的虚拟三维环境可访问性技术的优势与局限，结果表明该系统在取得积极成效的同时，仍面临系统可靠性与用户信任度方面的挑战。