游戏和模拟中使用的交互式 3D 资产通常被分解为特定的语义部分，以支持动画, 物理, 和脚本化行为,，但大多数生成 3D 模型要么生成整体网格，要么生成任意部分分解，无法满足特定于应用程序的要求。我们提出了 CubePart, 一个用于开放词汇, 零件可控 3D 网格生成的生成框架，它将零件结构公开为显式推理时间控制信号。给定全局文本提示和用户定义的部件模式，表示为部件名称的开放式列表,，我们的方法生成一组网格（每个模式元素一个），这些网格组装成一个连贯的对象，同时尊重指定的语义结构。为了实现此功能,，我们引入了一个可扩展的数据管道来构建大型开放词汇, 零件标记的 3D 数据集, 以及将全局形状合成与零件级解码分开的两阶段生成架构。我们证明，生成的资产可以直接集成到游戏引擎中，并由动画和行为脚本驱动，无需手动后处理。项目页面: 此 https URL
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: this https URL
科目:人工智能(cs.AI)
Subjects: Artificial Intelligence (cs.AI)