AnchorFlow: Editable SVG Reconstruction via Sparse Anchor Point Fields

TL;DR

AnchorFlow is a novel framework for SVG reconstruction from raster images that optimizes anchor point placement to improve editability and fidelity, addressing structural trade-offs in vector graphics.

Contribution

It introduces sparse anchor point fields for path-level anchor placement, enhancing SVG reconstruction's fidelity and editability compared to prior methods.

Findings

Reduces editable complexity while maintaining raster fidelity.

Achieves a favorable fidelity-editability trade-off.

Outperforms existing methods in experiments.

Abstract

Image-to-SVG reconstruction aims to produce vector graphics that are faithful to raster inputs and easy to edit. Existing methods face a structural trade-off in how vector structure is parameterized, including how many paths represent an image and how many anchor points define each path. High-fidelity methods often rely on many paths or densely parameterized curves, whereas overly compact SVG generation may deviate from the input geometry. This issue becomes more pronounced when local raster evidence is imperfect, where boundary-following reconstruction can introduce redundant anchors and fragmented structures. We argue that this trade-off should be addressed at the level of anchor placement, since anchors on Bezier curves define local path structure and strongly affect both accuracy and editability. We propose AnchorFlow, an editable SVG reconstruction framework that models path-level anchor placement with sparse anchor point fields. Given path-like foreground components extracted from a raster image, AnchorFlow predicts an image-conditioned sparse anchor field for each component and resolves it into an ordered Bezier path. Rendering-guided feedback then corrects local structural errors before re-resolution. The recovered paths are then assembled and optimized into the final SVG. Experiments on isolated paths and full images show that AnchorFlow achieves a favorable fidelity-editability trade-off, substantially reducing editable complexity while preserving competitive raster fidelity.