LayerPeeler: Autoregressive Peeling for Layer-wise Image Vectorization
Ronghuan Wu, Wanchao Su, Jing Liao

TL;DR
LayerPeeler introduces an autoregressive layer peeling method for image vectorization that effectively handles occlusions, producing complete, coherent vector graphics by leveraging vision-language models and diffusion-based editing.
Contribution
The paper presents a novel layer-wise vectorization approach using autoregressive peeling and vision-language models, improving quality and flexibility over existing methods.
Findings
Outperforms existing vectorization techniques in quality and coherence.
Generates vector graphics with complete paths and clear layer structures.
Achieves superior geometric regularity and visual fidelity.
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 optimization-based and learning-based 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…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Computer Graphics and Visualization Techniques · Multimodal Machine Learning Applications
MethodsSoftmax · Attention Is All You Need · Diffusion
