一句话生成可探索的3D世界，还能拆开改
现在的AI生成3D世界，要么是整块贴图、要么是零散物件，你没法进去逛，更没法把里面某个房子单独拎出来改。这篇的WorldClaw把流程拆成“先规划再搭建”：先让AI把文字描述翻译成一张区域清单——哪里是山、哪里是路、哪里放房子，然后按清单先铺地形，再往每个区域里填细节，最后用渲染反馈把物体和地面的接触修自然。结果是：世界是连贯的、能走进去的，而且每个物体都是独立可编辑的资产。它不是你明天就能用的工具，但“可编辑的开放世界”是游戏和影视制作真正缺的那块。
📄 原文摘要(英文)
Generating large-scale, freely explorable 3D worlds from open-ended text remains challenging because a system must jointly maintain global spatial coherence, rich local content, and explicit assets suitable for downstream editing and reuse. We present WorldClaw, a fully agentic, coarse-to-fine framework for open-world 3D scene generation. Planning agents translate a text prompt into a structured specification of regions, terrain, assets, materials, and spatial relations. WorldClaw then builds a globally coherent terrain foundation from semantic layouts, reusable assets, generative or procedural materials, and a region-aware height field. For detail-demanding regions, it generates terrain-conditioned compositions, reconstructs editable textured meshes, and recovers their placement on the terrain; render-based agents further refine terrain, objects, appearance, and contacts. Across diverse open-world prompts, WorldClaw produces large-scale scenes with coherent spatial organization, visually compelling local content, and editable instance-level assets while preserving a consistent global terrain structure.