用草图自动建模3D物体的可动部件和运动参数
Sketch2Arti: Sketch-based Articulation Modeling of CAD Objects

- 用户画草图指示部件运动方向,系统自动生成可动结构
- 无需类别信息,能泛化到多种复杂物体,支持精细控制
- 可基于草图补全内部结构,保持几何与运动一致性
articulation modeling 旨在推断3D物体的可动部件及其运动参数,支持交互式动画、仿真和形状编辑。本文提出 Sketch2Arti,首个基于草图的CAD物体可动性建模系统。核心观察是设计师常通过箭头、线条等轻量草图表达部件运动意图,但将此类草图转化为可动3D模型仍需大量手动操作。Sketch2Arti 通过用户从选定视角绘制的简单2D草图,自动识别对应可动部件并预测其运动参数,支持在复杂物体上进行多轮迭代建模,实现细粒度控制。重要的是,Sketch2Arti 采用类别无关训练方式,无需物体类别信息,具备强泛化能力,适用于现有数据集之外的多样化对象。此外,对于缺乏内部结构的壳体模型,Sketch2Arti 可在用户草图引导下进行可控内部补全,生成与已有几何和预测运动约束一致的合理内部组件。全面实验与用户评估验证了 Sketch2Arti 的有效性、可控性与泛化能力。代码、数据集及原型系统见 https://arlo-yang.github.io/Sketch2Arti。
原文摘要 · Abstract (English)
Articulation modeling aims to infer movable parts and their motion parameters for a 3D object, enabling interactive animation, simulation, and shape editing. In this paper, we present Sketch2Arti, the first sketch-based articulation modeling system for CAD objects. Our key observation is that designers naturally communicate articulation intent through lightweight sketches (e.g., arrows and strokes) that indicate how parts should move, yet translating such sketches into articulated 3D models remains largely manual. Sketch2Arti bridges this gap by enabling users to specify articulation through simple 2D sketches drawn from a chosen viewpoint. Given a CAD model and user sketches, our approach automatically discovers the corresponding movable parts and predicts their motion parameters, allowing iterative modeling of multiple articulations on complex objects with fine-grained control. Importantly, Sketch2Arti is trained in a category-agnostic manner without requiring object category information, leading to strong generalization to diverse objects beyond existing articulation datasets. Moreover, for shell models lacking interior structures, Sketch2Arti supports controllable internal completion guided by user sketches, generating plausible internal components consistent with the existing geometry and predicted motion constraints. Comprehensive experiments and user evaluations demonstrate the effectiveness, controllability, and generalization of Sketch2Arti. The code, dataset, and the prototype system are at https://arlo-yang.github.io/Sketch2Arti.
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