Sketch2Arti: Sketch-based Articulation Modeling of CAD Objects
Yi Yang, Hao Pan, Yijing Cui, Alla Sheffer, Changjian Li
TLDR
Sketch2Arti is a novel system that allows users to define 3D object articulations using simple 2D sketches, automating motion parameter inference.
Key contributions
- First system enabling sketch-based articulation modeling for CAD objects.
- Automatically infers movable parts and motion parameters from simple 2D user sketches.
- Achieves strong generalization across diverse objects via category-agnostic training.
- Supports sketch-guided internal completion for shell models, generating plausible components.
Why it matters
This paper simplifies complex 3D articulation modeling by translating intuitive 2D sketches into functional 3D movements. It significantly reduces manual effort, enabling faster interactive animation, simulation, and shape editing for diverse CAD objects.
Original Abstract
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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