SPHERE: Adaptive VR Indoor Scene Generation via LLM-Enhanced Spatial Preference Learning and Human-in-the-Loop RL· SPHERE: 基于大模型和强化学习的VR室内场景生成
While Large Language Models (LLMs) advance 3D indoor scene synthesis, current pipelines fail to retain user-specific preferences across sessions, making immersive authoring a repetitive and physically fatiguing process. We present SPHERE, an adaptive VR generation framework that transforms isolated synthesis into continuous human-AI co-creation. SPHERE extracts persistent spatial preferences from natural multimodal interactions (speech and controller edits). To ensure geometric resilience against spatial distortions, it abstracts these raw edits into hierarchical constraints modeling both local functional and global topological contexts. Furthermore, a human-in-the-loop reinforcement learning mechanism dynamically updates retrieval policies based on the user's final edited scenes. A mixed-
基于大模型和强化学习生成符合用户偏好的自适应VR室内场景。
- 核心方法
- SPHERE框架通过自然多模态交互提取持久的空间偏好，并将其抽象为分层约束，结合用户在环强化学习机制动态更新检索策略。
- 适合谁读
- 研究者、工程师、产品设计师
- 要解决的问题
- 现有3D室内场景生成方法无法保留用户偏好，导致重复性和体力消耗的创作过程。
- 关键实验
- 未提供
- 主要贡献
- 实现了用户特定偏好在不同创作会话间的保留，提高了VR室内场景生成的沉浸感和效率。
- 意义与局限
- SPHERE促进了持续的人机共创，可能改变未来的室内设计和VR用户体验方式，但需要更多实验验证其稳定性和泛化能力。