HOG-Layout：通过视觉-语言模型实现层次化3D场景生成、优化与编辑
HOG-Layout: Hierarchical 3D Scene Generation, Optimization and Editing via Vision-Language Models
AI总结：
HOG-Layout利用大语言模型和视觉-语言模型实现文本驱动的层次化3D场景生成、优化与实时编辑，通过检索增强生成技术提升语义一致性，结合优化模块增强物理一致性，实现高效场景编辑。
摘要：
3D布局生成和编辑在具身AI和沉浸式VR交互中起着关键作用。然而，手动创建需要大量劳动，而数据驱动生成往往缺乏多样性。大模型的出现为3D场景合成带来了新可能。我们提出了HOG-Layout，该方法利用大语言模型（LLMs）和视觉-语言模型（VLMs）实现文本驱动的层次化场景生成、优化和实时场景编辑。HOG-Layout通过检索增强生成（RAG）技术提高场景语义一致性和合理性，结合优化模块增强物理一致性，并采用层次化表示以提升推理和优化效率，实现实时编辑。实验结果表明，HOG-Layout生成的环境相比现有基线更为合理，同时支持快速直观的场景编辑。
英文摘要：
3D layout generation and editing play a crucial role in Embodied AI and immersive VR interaction. However, manual creation requires tedious labor, while data-driven generation often lacks diversity. The emergence of large models introduces new possibilities for 3D scene synthesis. We present HOG-Layout that enables text-driven hierarchical scene generation, optimization and real-time scene editing with large language models (LLMs) and vision-language models (VLMs). HOG-Layout improves scene semantic consistency and plausibility through retrieval-augmented generation (RAG) technology, incorporates an optimization module to enhance physical consistency, and adopts a hierarchical representation to enhance inference and optimization, achieving real-time editing. Experimental results demonstrate that HOG-Layout produces more reasonable environments compared with existing baselines, while supporting fast and intuitive scene editing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
