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Computer Science > Computer Vision and Pattern Recognition

arXiv:2505.23740 (cs)
[Submitted on 29 May 2025]

Title:LayerPeeler: Autoregressive Peeling for Layer-wise Image
Vectorization

Authors:Ronghuan Wu, Wanchao Su, Jing Liao
View a PDF of the paper titled LayerPeeler: Autoregressive Peeling
for Layer-wise Image Vectorization, by Ronghuan Wu and 2 other
authors
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    Abstract:Image vectorization is a powerful technique that
    converts raster images into vector graphics, enabling enhanced
    flexibility and interactivity. However, popular image
    vectorization tools struggle with occluded regions, producing
    incomplete or fragmented shapes that hinder editability. While
    recent advancements have explored rule-based and data-driven
    layer-wise image vectorization, these methods face limitations in
    vectorization quality and flexibility. In this paper, we
    introduce LayerPeeler, a novel layer-wise image vectorization
    approach that addresses these challenges through a progressive
    simplification paradigm. The key to LayerPeeler's success lies in
    its autoregressive peeling strategy: by identifying and removing
    the topmost non-occluded layers while recovering underlying
    content, we generate vector graphics with complete paths and
    coherent layer structures. Our method leverages vision-language
    models to construct a layer graph that captures occlusion
    relationships among elements, enabling precise detection and
    description for non-occluded layers. These descriptive captions
    are used as editing instructions for a finetuned image diffusion
    model to remove the identified layers. To ensure accurate
    removal, we employ localized attention control that precisely
    guides the model to target regions while faithfully preserving
    the surrounding content. To support this, we contribute a
    large-scale dataset specifically designed for layer peeling
    tasks. Extensive quantitative and qualitative experiments
    demonstrate that LayerPeeler significantly outperforms existing
    techniques, producing vectorization results with superior path
    semantics, geometric regularity, and visual fidelity.

Comments: Project Page: this https URL
Subjects: Computer Vision and Pattern Recognition (cs.CV); Graphics
          (cs.GR)
Cite as:  arXiv:2505.23740 [cs.CV]
          (or arXiv:2505.23740v1 [cs.CV] for this version)
          https://doi.org/10.48550/arXiv.2505.23740
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Ronghuan Wu [view email]
[v1] Thu, 29 May 2025 17:58:03 UTC (2,376 KB)
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