# HCVR Scene Generation: High Compatibility Virtual Reality Environment Generation for Extended Redirected Walking

arXiv: https://arxiv.org/abs/2601.14679 · HTML: https://mlsanity.com/paper/2601.14679/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 60 references that are in the map.
- First posted on arXiv 21 Jan 2026.

## Builds on (most cited first, 20 of 20)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?](https://mlsanity.com/paper/2403.14624/) (2024)
- [LayoutGPT: Compositional Visual Planning and Generation with Large Language Models](https://mlsanity.com/paper/2305.15393/) (2023)
- [Holodeck: Language Guided Generation of 3D Embodied AI Environments](https://mlsanity.com/paper/2312.09067/) (2023)
- [SceneFormer: Indoor Scene Generation with Transformers](https://mlsanity.com/paper/2012.09793/) (2020)
- [LayoutTransformer: Layout Generation and Completion with Self-attention](https://mlsanity.com/paper/2006.14615/) (2020)
- [LayoutVAE: Stochastic Scene Layout Generation From a Label Set](https://mlsanity.com/paper/1907.10719/) (2019)
- [LayoutVLM: Differentiable Optimization of 3D Layout via Vision-Language Models](https://mlsanity.com/paper/2412.02193/) (2024)
- [I-Design: Personalized LLM Interior Designer](https://mlsanity.com/paper/2404.02838/) (2024)
- [Evaluating Spatial Understanding of Large Language Models](https://mlsanity.com/paper/2310.14540/) (2023)
- [Modeling Image Composition for Complex Scene Generation](https://mlsanity.com/paper/2206.00923/) (2022)
- [SceneCraft: Layout-Guided 3D Scene Generation](https://mlsanity.com/paper/2410.09049/) (2024)
- [Towards Language-guided Interactive 3D Generation: LLMs as Layout Interpreter with Generative Feedback](https://mlsanity.com/paper/2305.15808/) (2023)
- [TrustNavGPT: Modeling Uncertainty to Improve Trustworthiness of Audio-Guided LLM-Based Robot Navigation](https://mlsanity.com/paper/2408.01867/) (2024)
- [Learning Beyond Pattern Matching? Assaying Mathematical Understanding in LLMs](https://mlsanity.com/paper/2405.15485/) (2024)
- [Beyond Text: Utilizing Vocal Cues to Improve Decision Making in LLMs for Robot Navigation Tasks](https://mlsanity.com/paper/2402.03494/) (2024)
- [MVGaussian: High-Fidelity text-to-3D Content Generation with Multi-View Guidance and Surface Densification](https://mlsanity.com/paper/2409.06620/) (2024)
- [Redirected Walking in Static and Dynamic Scenes Using Visibility Polygons](https://mlsanity.com/paper/2106.06807/) (2021)
- [ARC: Alignment-based Redirection Controller for Redirected Walking in Complex Environments](https://mlsanity.com/paper/2101.04912/) (2021)
- [ENI: Quantifying Environment Compatibility for Natural Walking in Virtual Reality](https://mlsanity.com/paper/2201.01261/) (2022)

## Abstract (arXiv)

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.