MultiCube：具有部件级语义与空间控制的组合式三维生成

中文摘要 AI 辅助

游戏和动画中使用的数字三维对象常需具备组合性，即分解为语义上有意义的部件。现有三维生成方法可基于图像或文本提示生成高质量的组合式对象，但这类全局提示缺乏专业创意工作流所需的精确部件级可控性。为解决该问题，本文提出MultiCube，一种新颖的组合式三维生成方法，可对每个部件的语义和空间排布提供明确、独立的控制。MultiCube的输入包括全局文本提示、指定所需部件的文本模式、以及指示给定模式中各部件边界框的空间布局；其输出为由不同网格组成的三维对象，每个网格对应一个指定部件，且符合给定的语义和空间条件。该方法采用两阶段扩散过程：首先生成与模式和布局对齐的整体网格，再将该网格同时分解为单个部件；其中采用了一种新颖的部件布局适配器，用于独立编码各部件的条件，不受其他部件影响。实验表明，该方法可生成具有精确部件级控制的高质量组合式三维对象，包括仅通过文本或图像提示难以实现的独特布局的对象。项目页面：this https URL

英文摘要

Digital 3D objects used in games and animation are often required to be compositional; that is, decomposed into semantically meaningful parts. Recent 3D generation methods can produce high-quality compositional objects conditioned on image or text prompts. Yet, such global conditioning lacks the precise part-level controllability required for professional creative workflows. To address this, we introduce MultiCube, a novel compositional 3D generation method that provides explicit, independent control over both the semantics and spatial arrangement of each part. MultiCube takes as input a global text prompt, a text schema specifying the desired parts, and a spatial layout indicating the bounding boxes of the parts in the given schema. It outputs a 3D object composed of distinct meshes, one per specified part, that adhere to the given semantic and spatial conditions. Our approach employs a two-stage diffusion process, first generating a schema- and layout-aligned monolithic mesh, then decomposing the mesh into individual parts simultaneously. A novel Part Layout Adapter is used to encode per-part conditions independently of the other parts. Experiments demonstrate that our method can generate high-quality compositional 3D objects with precise part-level control, including those with unique layouts difficult to achieve with text or image prompting alone. Project page: https://multi-cube.github.io

发表机构

• Roblox（罗布乐思（Roblox））
• Carnegie Mellon University（卡内基梅隆大学）
• Stanford University（斯坦福大学）

机构由 AI 辅助整理，请以论文原文为准。