Abstract
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.
Abstract (translated)
自然行走可以增强虚拟环境（VE）中的沉浸感，但物理空间的限制和障碍物会阻碍探索，尤其是在大型虚拟场景中。重定向行走（RDW）技术通过微妙地操控虚拟摄像机来引导用户远离预定义虚拟环境中不可避免的身体碰撞，从而缓解这一问题。然而，当物理和虚拟环境之间存在显著的几何差异时，RDW的有效性会大幅下降，导致无法避免的碰撞发生。现有的场景生成方法主要关注对象之间的关系或布局美学，常常忽略了有效实现RDW所需的至关重要的物理兼容性方面。 为解决这个问题，我们提出了HCVR（高兼容性的虚拟现实环境生成），这是一种新颖的框架，能够根据基于对齐的重定向行走控制器的需求来生成优化后的虚拟场景。HCVR首先使用ENI++这一新提出的、具备边界敏感特性的度量标准来评估物理空间和虚拟空间之间的不兼容性，通过对比旋转敏感可见多边形来实现这一点。在ENI++兼容地图和用户提示的指导下，HCVR利用大型语言模型（LLM）进行上下文感知的3D资产检索以及初始布局生成。 随后，该框架策略地调整对象选择、缩放和平移以最大化虚拟不兼容区域的覆盖面积，从而有效地引导用户走向可行的重定向行走路径。通过评估物理碰撞和布局质量的用户研究显示，与仅使用LLM进行基于RDW的场景生成相比，HCVR生成的场景中的物理碰撞减少了22.78倍，并且在ENI++评分上降低了35.89%，同时从用户对布局设计反馈的评分中也获得了12.5%的增长。
URL
https://arxiv.org/abs/2601.14679
