Graphic icons are a cornerstone of modern design workflows, yet they are often distributed as flattened single-path or compound-path graphics, where the original semantic layering is lost. This absence of semantic decomposition hinders downstream tasks such as editing, restyling, and animation. We formalize this problem as semantic layer construction for flattened vector art and introduce SemLayer, a visual generation empowered pipeline that restores editable layered structures. Given an abstract icon, SemLayer first generates a chromatically differentiated representation in which distinct semantic components become visually separable. To recover the complete geometry of each part, including occluded regions, we then perform a semantic completion step that reconstructs coherent object-level shapes. Finally, the recovered parts are assembled into a layered vector representation with inferred occlusion relationships. Extensive qualitative comparisons and quantitative evaluations demonstrate the effectiveness of SemLayer, enabling editing workflows previously inapplicable to flattened vector graphics and establishing semantic layer reconstruction as a practical and valuable task. Project page: https://xxuhaiyang.github.io/SemLayer/

翻译：摘要：图形图标是现代设计工作流的基石，但它们通常以单路径或复合路径压平后的图形形式分发，原始语义分层信息丢失。这种语义分解的缺失阻碍了编辑、风格迁移和动画等下游任务。我们将此问题形式化为压平矢量艺术的语义图层构建任务，并引入SemLayer——一种视觉生成驱动的流水线，可恢复可编辑的分层结构。给定一个抽象图标，SemLayer首先生成一个色差编码表示，使不同语义组件在视觉上可分离。为恢复每个部分的完整几何形状（包括被遮挡区域），我们随后执行语义补全步骤，重建连贯的对象级形状。最终，恢复的部分被组装成具有推断遮挡关系的分层矢量表示。广泛的定性比较与定量评估证明了SemLayer的有效性，它使得此前无法应用于压平矢量图形的编辑工作流成为可能，并将语义图层重建确立为一项实用且有价值的任务。项目页面：https://xxuhaiyang.github.io/SemLayer/