让3D生成按用户定义的部件结构精准组装，无需后期处理。
CubePart: An Open-Vocabulary Part-Controllable 3D Generator
- 用开放词汇表定义部件名称，生成对应分块网格。
- 支持游戏引擎直接导入，装配后可直接驱动动画与行为。
- 构建大规模部件标注数据集，分离全局形状与局部解码流程。
交互式3D资产在游戏和仿真中通常需分解为特定语义部件以支持动画、物理和脚本行为，但现有生成模型多产出整体网格或任意部件划分，无法对齐应用需求。本文提出CubePart，一种开放词汇、部件可控的3D网格生成框架，将部件结构作为推理时的显式控制信号。给定全局文本提示和用户自定义的部件列表（开放词汇），方法生成一组网格（每项对应一个部件），可组装成连贯物体并符合指定语义结构。为此，我们构建了大规模开放词汇部件标注3D数据集，并设计两阶段生成架构：先合成全局形状，再解码部件级细节。实验表明，生成资产可直接集成至游戏引擎，无需人工后处理即可驱动动画与行为脚本。项目页面：https://cubepart.github.io/
原文摘要 · Abstract (English)
Interactive 3D assets used in games and simulation are typically decomposed into specific semantic parts to support animation, physics, and scripted behaviors, yet most generative 3D models produce either monolithic meshes or arbitrary part decompositions that cannot be aligned with application-specific requirements. We present CubePart, a generative framework for open-vocabulary, part-controllable 3D mesh generation that exposes part structure as an explicit inference-time control signal. Given a global text prompt and a user-defined parts schema expressed as an open-ended list of part names, our method generates a set of meshes - one per schema element - that assemble into a coherent object while respecting the specified semantic structure. To enable this capability, we introduce a scalable data pipeline to construct a large open-vocabulary, part-labeled 3D dataset, along with a two-stage generative architecture that separates global shape synthesis from part-level decoding. We demonstrate that the resulting assets can be directly integrated into game engines and driven by animation and behavior scripts without manual post-processing. Project Page: https://cubepart.github.io/
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。