SceneFoundry: 生成交互式无限3D世界
SceneFoundry: Generating Interactive Infinite 3D Worlds
AI总结：
SceneFoundry通过语言引导的扩散框架生成功能性强的3D环境，用于机器人训练和具身智能研究。
摘要：
自动生成大规模、交互式且物理真实的3D环境能力对于推进机器人学习和具身智能至关重要。然而，现有生成方法往往无法捕捉真实世界内部的复杂功能，特别是包含可动部件的室内环境，这些部件对于操作和导航至关重要。本文提出了SceneFoundry，一种语言引导的扩散框架，能够生成公寓级的3D世界，包含功能性的可动家具和语义多样的布局，用于机器人训练。从自然语言提示出发，一个LLM模块控制地板布局生成，而基于扩散的后验采样高效地在场景中填充来自大规模3D仓库的可动资产。为了确保物理可用性，SceneFoundry采用可微指导函数来调节物体数量、防止关节碰撞，并保持足够的可行走空间以供机器人导航。广泛的实验表明，我们的框架在多种场景类型和条件下生成结构有效、语义一致且功能交互的环境，使可扩展的具身AI研究成为可能。项目页面：https://anc891203.github.io/SceneFoundry-Demo/
英文摘要：
The ability to automatically generate large-scale, interactive, and physically realistic 3D environments is crucial for advancing robotic learning and embodied intelligence. However, existing generative approaches often fail to capture the functional complexity of real-world interiors, particularly those containing articulated objects with movable parts essential for manipulation and navigation. This paper presents SceneFoundry, a language-guided diffusion framework that generates apartment-scale 3D worlds with functionally articulated furniture and semantically diverse layouts for robotic training. From natural language prompts, an LLM module controls floor layout generation, while diffusion-based posterior sampling efficiently populates the scene with articulated assets from large-scale 3D repositories. To ensure physical usability, SceneFoundry employs differentiable guidance functions to regulate object quantity, prevent articulation collisions, and maintain sufficient walkable space for robotic navigation. Extensive experiments demonstrate that our framework generates structurally valid, semantically coherent, and functionally interactive environments across diverse scene types and conditions, enabling scalable embodied AI research. project page: https://anc891203.github.io/SceneFoundry-Demo/
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。
