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MeshPad: Interactive Sketch-Conditioned Artist-Reminiscent Mesh Generation and Editing

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arxiv 2503.01425 v4 pith:ZAFZSYPI submitted 2025-03-03 cs.GR cs.CV

MeshPad: Interactive Sketch-Conditioned Artist-Reminiscent Mesh Generation and Editing

classification cs.GR cs.CV
keywords mesheditingapproacheditsgenerationmeshpadtriangleaddition
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We introduce MeshPad, a generative approach that creates 3D meshes from sketch inputs. Building on recent advances in artist-reminiscent triangle mesh generation, our approach addresses the need for interactive mesh creation. To this end, we focus on enabling consistent edits by decomposing editing into 'deletion' of regions of a mesh, followed by 'addition' of new mesh geometry. Both operations are invoked by simple user edits of a sketch image, facilitating an iterative content creation process and enabling the construction of complex 3D meshes. Our approach is based on a triangle sequence-based mesh representation, exploiting a large Transformer model for mesh triangle addition and deletion. In order to perform edits interactively, we introduce a vertex-aligned speculative prediction strategy on top of our additive mesh generator. This speculator predicts multiple output tokens corresponding to a vertex, thus significantly reducing the computational cost of inference and accelerating the editing process, making it possible to execute each editing step in only a few seconds. Comprehensive experiments demonstrate that MeshPad outperforms state-of-the-art sketch-conditioned mesh generation methods, achieving more than 22% mesh quality improvement in Chamfer distance, and being preferred by 90% of participants in perceptual evaluations.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. MeshFlow: Mesh Generation with Equivariant Flow Matching

    cs.GR 2026-06 unverdicted novelty 7.0

    MeshFlow applies equivariant optimal-transport flow matching to generate triangle meshes as soups, matching autoregressive quality with an 18x inference speedup.

  2. EditVerse3D: High-Quality 3D Object Editing with Region-Aware Learning

    cs.CV 2026-07 conditional novelty 6.0

    An end-to-end 3D editing framework achieves high-fidelity local edits from coarse bounding boxes and 2D image prompts using region-aware loss reweighting and a large-scale parts-derived training dataset.

  3. 3DMorph: Single-Image-Guided Local 3D Shape Editing and Morphing

    cs.CV 2026-06 unverdicted novelty 6.0

    3DMorph transfers local modifications from a single edited 2D image to the corresponding regions of a 3D mesh without training and supports shape morphing between original and edited versions.