Natural walking enhances immersion in virtual environments (VEs), but physical space limitations and obstacles hinder exploration, especially in large virtual scenes. Redirected Walking (RDW) techniques mitigate this by subtly manipulating the virtual camera to guide users away from physical collisions within pre-defined VEs. However, RDW efficacy diminishes significantly when substantial geometric divergence exists between the physical and virtual environments, leading to unavoidable collisions. Existing scene generation methods primarily focus on object relationships or layout aesthetics, often neglecting the crucial aspect of physical compatibility required for effective RDW. To address this, we introduce HCVR (High Compatibility Virtual Reality Environment Generation), a novel framework that generates virtual scenes inherently optimized for alignment-based RDW controllers. HCVR first employs ENI++, a novel, boundary-sensitive metric to evaluate the incompatibility between physical and virtual spaces by comparing rotation-sensitive visibility polygons. Guided by the ENI++ compatibility map and user prompts, HCVR utilizes a Large Language Model (LLM) for context-aware 3D asset retrieval and initial layout generation. The framework then strategically adjusts object selection, scaling, and placement to maximize coverage of virtually incompatible regions, effectively guiding users towards RDW-feasible paths. User studies evaluating physical collisions and layout quality demonstrate HCVR's effectiveness with HCVR-generated scenes, resulting in 22.78 times fewer physical collisions and received 35.89\% less on ENI++ score compared to LLM-based generation with RDW, while also receiving 12.5\% higher scores on user feedback to layout design.

翻译：自然行走能增强用户在虚拟环境中的沉浸感，但物理空间限制与障碍物会阻碍探索，尤其是在大型虚拟场景中。重定向行走技术通过细微操控虚拟相机引导用户避开物理碰撞，从而缓解这一问题。然而，当物理环境与虚拟环境之间存在显著的几何差异时，重定向行走的效能会大幅下降，导致不可避免的碰撞。现有的场景生成方法主要关注物体关系或布局美观性，往往忽视了有效重定向行走所需的关键物理兼容性。为此，我们提出HCVR（高兼容性虚拟现实环境生成），一种新颖的框架，能够生成本质上为基于对齐的重定向行走控制器优化的虚拟场景。HCVR首先采用ENI++这一新颖的边界敏感度量，通过比较旋转敏感可视性多边形来评估物理空间与虚拟空间之间的不兼容性。在ENI++兼容性地图和用户提示的引导下，HCVR利用大型语言模型进行上下文感知的3D资产检索与初始布局生成。该框架随后策略性地调整物体选择、缩放与放置，以最大化覆盖虚拟不兼容区域，从而有效引导用户走向可实施重定向行走的路径。评估物理碰撞与布局质量的用户研究表明，HCVR生成场景的效果显著：与基于大型语言模型结合重定向行走的生成方法相比，HCVR生成场景的物理碰撞次数减少了22.78倍，ENI++得分降低了35.89%，同时在布局设计的用户反馈中获得了12.5%更高的评分。