Sketch2Arti: Sketch-based Articulation Modeling of CAD Objects

arXiv cs.CV / 4/29/2026

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Key Points

  • The paper introduces Sketch2Arti, a new sketch-based system that converts 2D user sketches into articulated 3D CAD models by inferring movable parts and their motion parameters.
  • It leverages the insight that designers often express articulation intent with simple sketch primitives (e.g., strokes and arrows), reducing the largely manual step of translating intent into articulated geometry.
  • Sketch2Arti supports iterative articulation modeling on complex objects with fine-grained control, taking a chosen viewpoint and user drawings as inputs.
  • The method is trained category-agnostically, enabling strong generalization to CAD objects beyond the categories covered by existing articulation datasets.
  • For shell models without interior structure, it can perform user-guided internal completion that generates plausible internal components consistent with geometry and predicted motion constraints.

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.