让扁平图标重新拥有可编辑的语义图层
SemLayer: Semantic-aware Generative Segmentation and Layer Construction for Abstract Icons
- 通过色彩区分语义组件，使抽象图标各部分可见分离
- 补全遮挡区域几何，恢复完整物体形状
- 自动生成带遮挡关系的矢量分层结构，适合设计编辑
图形图标是现代设计流程的核心，但通常以单路径或复合路径的扁平化矢量形式存在，原始语义分层信息丢失。这导致编辑、重风格化和动画等下游任务难以开展。本文将问题形式化为扁平矢量艺术的语义分层重建，提出SemLayer——一种基于视觉生成的流水线，用于恢复可编辑的分层结构。给定一个抽象图标，SemLayer首先生成色彩差异化表示，使不同语义组件在视觉上可分离；接着通过语义补全步骤，重建每个部分的完整几何结构（包括被遮挡区域）；最后将恢复的部分组装成带有推断遮挡关系的分层矢量表示。大量定性对比与定量评估表明，SemLayer有效实现了此前无法应用于扁平矢量图形的编辑工作流，确立了语义分层重建为一项实用且有价值的任务。
原文摘要 · Abstract (English)
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/
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