3D模型终于不只是好看，还会动了
现在的3D生成模型能造出好看的物体，但造出来的门一推就穿墙、椅子一坐就塌。这篇把目标从「长得像」改成「动起来也对」:用多智能体流水线，在生成过程中加入物理模拟反馈，让模型自己学会避开碰撞、站稳、算对重量和材质。他们建了100个参考资产的基准，测空间、物理、下游任务和整体质量，结果碰撞率最低、稳定率最高。它不是你明天能直接用的工具，但这是3D内容从「摆设」走向「能用」的关键一步——游戏、机器人仿真、工业设计里那些需要真实物理交互的场景，都会因此受益。
📄 原文摘要(英文)
In this paper, we study physically grounded articulated 3D asset generation---moving the goal from reproducing how objects look to capturing how they move and function. Inspired by the literature on procedural and agentic generation, we present Artisan, a system that extends the latest method Articraft by introducing a multi-agent pipeline with physical simulation feedback, enabling comprehensive attributes beyond geometry, appearance, and physically plausible articulation, such as collision-free motion, stability under gravity, and accurate mass and material properties. Furthermore, we construct a unified benchmark spanning four key aspects: spatial fidelity, physical plausibility, downstream task performance, and overall quality. The benchmark encompasses 100 reference assets from ArtVIP and LightWheel, 50 stability prompts, and 50 ABO objects with reference masses, with protocols evaluating various metrics. Through extensive evaluation of more than 10 representative models, Artisan achieves competitive spatial fidelity while attaining the lowest collision rates and highest stability rates among the evaluated methods. Together, our system and benchmark establish insights for advancing 3D generation towards physically plausible, functionally usable assets.