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arxiv: 2604.25781 · v3 · pith:G4J6CGZVnew · submitted 2026-04-28 · 💻 cs.CV · cs.GR

Sketch2Arti: Sketch-based Articulation Modeling of CAD Objects

Yi Yang , Hao Pan , Yijing Cui , Alla Sheffer , Changjian Li This is my paper

Pith reviewed 2026-07-01 08:46 UTC · model grok-4.3

classification 💻 cs.CV cs.GR
keywords sketch-based modelingarticulation modelingCAD objectsmovable partsmotion predictioncategory-agnostic learninginternal structure completion
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The pith

Sketch2Arti maps simple 2D user sketches on a CAD model to 3D movable parts and motion parameters without category labels.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces Sketch2Arti as the first sketch-based system for articulation modeling of CAD objects. Users provide lightweight sketches such as arrows and strokes from a single viewpoint to indicate intended part movements. The system then identifies the corresponding movable parts, predicts their motion parameters, and supports iterative addition of multiple articulations on complex models. Training occurs in a category-agnostic manner, which enables generalization to objects outside existing articulation datasets. The method also generates plausible internal structures for shell models when guided by the same sketches.

Core claim

Given a CAD model and user sketches drawn from a chosen viewpoint, Sketch2Arti automatically discovers movable parts and predicts their motion parameters, allowing iterative modeling of multiple articulations on complex objects with fine-grained control; the system is trained category-agnostic without object category information and additionally supports controllable internal completion for shell models.

What carries the argument

A category-agnostic neural mapping that takes 2D sketches (arrows and strokes) together with the 3D CAD geometry and outputs corresponding movable parts plus motion parameters.

If this is right

  • Designers can add and refine multiple articulations iteratively on the same complex CAD object.
  • The resulting articulated models support downstream tasks such as interactive animation and simulation.
  • Internal structures can be completed for shell-type CAD models while remaining consistent with the predicted motions.
  • The approach applies to objects outside the categories seen in existing articulation datasets.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same sketch-driven interface could be adapted to scanned real-world objects if the underlying geometry representation is adjusted.
  • Integration into existing CAD tools would let users test motion constraints directly during the design loop.
  • The category-agnostic training suggests the method may scale to larger collections of unlabeled CAD data.
  • Extending the input to include multiple viewpoints or temporal sketch sequences could increase precision on ambiguous cases.

Load-bearing premise

Sketches drawn from one viewpoint can be mapped reliably to 3D parts and motions even when no category information is supplied during training.

What would settle it

Run the trained model on a held-out collection of CAD objects with independently verified ground-truth articulations and measure whether predicted parts and motion parameters match the verified ones at rates comparable to the reported experiments.

Figures

Figures reproduced from arXiv: 2604.25781 by Alla Sheffer, Changjian Li, Hao Pan, Yijing Cui, Yi Yang.

Figure 1
Figure 1. Figure 1: Sketch-based articulation modeling. We present Sketch2Arti, the first sketch-based system for articulation modeling of CAD objects. Sketch2Arti is versatile. Top: through iterative sketch-based editing, Sketch2Arti progressively discovers multiple movable parts and recovers their motion parameters on a complex car model. Middle-left: Sketch2Arti offers high controllability—e.g., a car door can be opened in… view at source ↗
Figure 2
Figure 2. Figure 2: Articulation modeling in the design field. In the product design workflow, designers frequently draw arrow-like strokes depicting the ar￾ticulation cues of man-made objects in the ideation stage. Other than the arrows, strokes representing the part after articulation (lid of the left con￾tainer) and unseen internal structure (the drawer of the right container) are drawn to express the geometry. The design … view at source ↗
Figure 4
Figure 4. Figure 4: User interface. The interface consists of an operation menu (top left, e.g., load an object), an interaction menu (top right, e.g., draw sketch), and a wide user interaction panel. After loading the object, users freely choose the desired view, select a focal field (green), draw strokes (red) indicating articulation intention, and click the “Finish & Predict” button to obtain the result. The green box show… view at source ↗
Figure 5
Figure 5. Figure 5: Overview. (a) Given an input 3D shape and the user sketches, our method Sketch2Arti addresses the where and how challenges by (b) identifying movable parts (i.e., the two doors) and inferring their articulation parameters. (c) The predicted motion reveals missing internal structure (e.g., an empty drawer), which users can further specify via sketches. Sketch2Arti then tackles the what challenge by (d) gene… view at source ↗
Figure 6
Figure 6. Figure 6: Articulation prediction. Given a static 3D object, we apply category-agnostic articulation recognition on a localized region surrounding the sketch with the local context captured by the depth and normal maps. A trained U-Net module predicts the articulation parameters in 2D maps and 3D local camera coordinates, as well as motion type. The 2D part mask is then back-projected onto the object surface and use… view at source ↗
Figure 7
Figure 7. Figure 7: Interior shape completion. Our approach leverages 2D and 3D generative models to complete the interior structures exposed by articulated parts. Given a 3D object with recognized articulation part and parameters, the top branch applies a 2D generative model (e.g., Nano banana) to obtain a high-quality reference image, which is used to guide the 3D generative model (e.g., Trellis) to create the interior stru… view at source ↗
Figure 8
Figure 8. Figure 8: Dataset gallery and statistics. Left: Representative samples from SketchMobility. Note the presence of uncommon articulated objects (e.g., helicopters and motorbikes), which are rarely considered in existing articulation modeling benchmarks. Right: Category distribution of SketchMobility. We report major categories (≥1.5%) individually, while merging minor categories into Others (18.9%). manually click to … view at source ↗
Figure 9
Figure 9. Figure 9: Sketch synthesis. (a) Given a 3D shape and its articulation, we construct 3D motion cues (e.g., hinge axis vectors and rotational arcs) to represent the motion of movable parts. (b) Directly projecting these 3D cues onto the image plane yields perfectly smooth curves, which are unrealistic for human freehand drawing. (c) We therefore inject pixel-level perturbations to obtain synthesized strokes that bette… view at source ↗
Figure 10
Figure 10. Figure 10: Results gallery. We show representative articulation modeling sessions using Sketch2Arti. For each example, user sketches are overlaid on the rendered shape under the chosen viewpoint, and the inferred movable parts are color-coded. The black arrow indicates the iterative modeling order across views/parts. datasheet [Gebru et al. 2021; Pushkarna et al. 2022] can be found there. 7 Results and Evaluation Us… view at source ↗
Figure 11
Figure 11. Figure 11: User gallery. We asked 5 participants to model the articulation of three objects–a toilet, oven, and car. With a few coarse strokes, all users achieved their desired articulation view at source ↗
Figure 12
Figure 12. Figure 12: Visual comparison. We show four representative examples com￾paring Singapo, FreeArt3D, and our method against the ground truth. and 15.3% over FreeArt3D and Singapo, respectively, while reducing CD by 21.3% and 43.6%. For motion estimation, Sketch2Arti fur￾ther yields substantial gains in articulation accuracy, improving the joint axis error by 56.2% / 13.1% and the joint pivot error by 53.7% / 35.6% comp… view at source ↗
Figure 13
Figure 13. Figure 13: Part segmentation. (a) Given a user sketch, we localize the target movable part using PartField features guided by the predicted part cues. (b) A k-means baseline yields infeasible segments with cross-part boundaries due to its flat clustering (see the purple segment and the ice outlet). (c) Our hierarchical strategy produces more plausible, part-consistent segments by organizing neighboring clusters in a… view at source ↗
Figure 14
Figure 14. Figure 14: Geometric snapping. We compare articulation predictions w/o (left) and w/ (right) geometric snapping. Top: microwave door articulation. Without snapping, the predicted axis/pivot slightly deviates from the hinge geometry, leading to misaligned opening. Snapping anchors the parameters to local geometric cues and yields a plausible hinge motion. Bottom: bicycle front-wheel articulation. Snapping refines the… view at source ↗
Figure 15
Figure 15. Figure 15: Ablation on masked completion for structure preservation. Masked completion of interior structures enables both the preservation of given static structures and the avoidance of extra erroneous content in the void. On the left, without masked completion, the cabinet has a drifted size and extra shape for the opened door. On the right, with masked com￾pletion, a clean cabinet of proper size and shape has be… view at source ↗
Figure 16
Figure 16. Figure 16: Ablation on iterated completion. For each test case, the iteration starts from the top left and continues to the down right. With each inter￾mediate shape one more pass of iterative generation is applied, gradually completing the interior structure. The difference between the initial com￾pletion and the final completion highlights the limited capacity of existing generative models for interior structure c… view at source ↗
Figure 18
Figure 18. Figure 18: Limitation. Opening an umbrella requires complex, coupled ar￾ticulation across many parts, which cannot be captured by our current single-joint motion model. deformation of the canopy. Since Sketch2Arti is designed to pre￾dict part-level rigid articulations with relatively constrained motion models, it cannot currently capture such complex, multi-part, and coupled mechanisms. Extending sketch-based articu… view at source ↗
read the 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper presents Sketch2Arti, the first sketch-based system for articulation modeling of CAD objects. Given a CAD model and lightweight 2D user sketches (arrows/strokes) drawn from a single chosen viewpoint, the method automatically discovers movable parts, predicts motion parameters (rotation/translation), supports iterative multi-articulation modeling with fine-grained control, operates in a fully category-agnostic manner, and performs controllable internal structure completion for shell models. The abstract asserts that comprehensive experiments and user studies demonstrate effectiveness, controllability, and generalization beyond existing articulation datasets.

Significance. If the core mapping from single-view sketches to 3D articulations holds with reliable accuracy and generalization, the work would introduce a practical, intuitive interface that reduces manual effort in articulation modeling for CAD, animation, and simulation pipelines. The category-agnostic training and internal-completion capability would be notable strengths for broad applicability.

major comments (2)
  1. [Abstract] Abstract: the central claim that single-view 2D sketches suffice to discover movable parts and predict motion parameters in a category-agnostic manner is geometrically under-constrained (multiple 3D axes, centers, and motion types are consistent with one 2D arrow once depth and segmentation are unknown), yet the abstract provides no explicit mechanism (multi-view consistency, geometric priors, uncertainty modeling, or learned disambiguation) that would resolve these ambiguities for arbitrary CAD geometry. This is load-bearing for the claims of reliable inference and strong generalization.
  2. [Abstract] Abstract: the assertion of 'comprehensive experiments and user evaluations' demonstrating effectiveness is stated without any methods description, dataset details, error metrics, baselines, or quantitative results, preventing verification of whether the reported generalization and controllability are supported by evidence or undermined by post-hoc choices.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their review and the opportunity to clarify points in the abstract. We respond to each major comment below, focusing on the manuscript's content and standard practices for abstracts.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that single-view 2D sketches suffice to discover movable parts and predict motion parameters in a category-agnostic manner is geometrically under-constrained (multiple 3D axes, centers, and motion types are consistent with one 2D arrow once depth and segmentation are unknown), yet the abstract provides no explicit mechanism (multi-view consistency, geometric priors, uncertainty modeling, or learned disambiguation) that would resolve these ambiguities for arbitrary CAD geometry. This is load-bearing for the claims of reliable inference and strong generalization.

    Authors: The abstract provides a concise overview and does not enumerate technical mechanisms, which are detailed in the method section. Sketch2Arti employs a neural network trained category-agnostically on diverse CAD articulation data; the learned priors, combined with consistency to the input CAD geometry and sketch projection, enable disambiguation of motion parameters. This data-driven approach supports the reported generalization without requiring multi-view input or explicit uncertainty modeling at inference time. revision: no

  2. Referee: [Abstract] Abstract: the assertion of 'comprehensive experiments and user evaluations' demonstrating effectiveness is stated without any methods description, dataset details, error metrics, baselines, or quantitative results, preventing verification of whether the reported generalization and controllability are supported by evidence or undermined by post-hoc choices.

    Authors: Abstracts are intentionally brief summaries and do not contain full experimental details by convention. The manuscript body includes dedicated sections describing the dataset construction, training procedure, quantitative metrics (e.g., motion parameter error, part segmentation IoU), baselines, ablation studies, and user study protocols that substantiate the claims of effectiveness and generalization. revision: no

Circularity Check

0 steps flagged

No circularity detected in derivation chain

full rationale

The paper describes an ML-based sketch-to-articulation system trained category-agnostically on data, with claims resting on empirical training, generalization tests, and user studies rather than any closed mathematical derivation. No equations, fitted parameters presented as predictions, self-definitional constructs, or load-bearing self-citations appear in the abstract or described approach. The central mapping from 2D sketches to 3D motions is handled via learned inference, not by construction from inputs, making the derivation self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no equations, training details, or modeling choices; no free parameters, axioms, or invented entities can be identified.

pith-pipeline@v0.9.1-grok · 5780 in / 1172 out tokens · 30476 ms · 2026-07-01T08:46:34.556590+00:00 · methodology

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