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.

翻译：图像到SVG重建旨在生成忠实于光栅输入且易于编辑的矢量图形。现有方法在矢量结构的参数化方式上面临结构性权衡，包括用多少路径表示图像以及每条路径由多少个锚点定义。高保真方法通常依赖大量路径或密集参数化曲线，而过于紧凑的SVG生成可能偏离输入几何形状。当局部光栅证据不完美时，这一问题更为突出——边界追踪重建可能引入冗余锚点和碎片化结构。我们认为这种权衡应从锚点布置层面解决，因为贝塞尔曲线上的锚点决定了局部路径结构，对精度和可编辑性均有显著影响。本文提出AnchorFlow——一种可编辑SVG重建框架，通过稀疏锚点场建模路径级锚点布置。给定从光栅图像中提取的类路径前景组件后，AnchorFlow为每个组件预测图像条件化稀疏锚点场，并将其解析为有序贝塞尔路径。渲染引导反馈在重新解析前修正局部结构错误，随后将恢复的路径组装并优化为最终SVG。在孤立路径和完整图像上的实验表明，AnchorFlow实现了保真度与可编辑性的良好平衡，在保持竞争性光栅保真度的同时大幅降低可编辑复杂度。