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/

翻译：用于游戏和仿真的交互式三维资产通常被分解为特定的语义部件，以支持动画、物理和脚本化行为，但大多数生成式三维模型要么生成整体网格，要么生成无法对齐应用特定需求的任意部件分解。我们提出CubePart，一种面向开放词汇、部件可控的三维网格生成的生成框架，它将部件结构作为显式的推理时控制信号。给定全局文本提示和用户定义的部件模式（以开放式部件名称列表表达），我们的方法生成一组网格——每个模式元素对应一个网格——这些网格在形成连贯对象的同时，遵循指定的语义结构。为实现此能力，我们引入一个可扩展的数据管线，用于构建大规模开放词汇且带部件标签的三维数据集，并采用两阶段生成架构，将全局形状合成与部件级解码分离。我们证明，生成的资产可直接集成到游戏引擎中，并由动画和行为脚本驱动，无需人工后处理。项目主页：https://cubepart.github.io/