Vector Scaffolding: Inter-Scale Orchestration for Differentiable Image Vectorization

• URL: http://arxiv.org/abs/2605.11913v1
• Date: Tue, 12 May 2026 10:27:30 GMT
• Title: Vector Scaffolding: Inter-Scale Orchestration for Differentiable Image Vectorization
• Abstract summary: We propose a novel hierarchical optimization framework that shifts from flat pixel-matching to structured topological construction tailored for vector graphics.<n> Experiments demonstrate that our approach accelerates optimization by $2.5times$ while simultaneously improving PSNR by up to 1.4 dB over the previous state of the art.
• Score: 48.98909774916165
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Differentiable vector graphics have enabled powerful gradient-based optimization of vector primitives directly from raster images. However, existing frameworks formulate this as a flat optimization problem, forcing hundreds to thousands of randomly initialized curves to blindly compete for pixel-level error reduction. This disordered optimization leads to topology collapse, where macroscopic structures are distorted by internal high-frequency noise, resulting in a redundant and uneditable "polygon soup" that limits practical editability. To address this limitation, we propose Vector Scaffolding, a novel hierarchical optimization framework that shifts from flat pixel-matching to structured topological construction tailored for vector graphics. By identifying a key cause of topology collapse as the mathematical imbalance between area and boundary gradients, we introduce Interior Gradient Aggregation to stabilize the learning dynamics of multi-scale curve mixtures. Upon this stabilized landscape, we employ Progressive Stratification and Rapid Inflation Scheduling to progressively densify vector primitives with extremely high learning rates ($\times 50$). Experiments demonstrate that our approach accelerates optimization by $2.5\times$ while simultaneously improving PSNR by up to 1.4 dB over the previous state of the art.

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