用扩散Transformer生成符合设计师思维的分层SVG，避免冗余且可编辑。
LayerTracer: Cognitive-Aligned Layered SVG Synthesis via Diffusion Transformer
- 基于文本条件的扩散Transformer生成分步渲染蓝图，模拟人类设计流程。
- 分层矢量化并去重路径，生成结构完整、可编辑的SVG文件。
- 适合需要高质量、可修改矢量图的设计场景，如UI/UX与插画创作。
生成符合认知的分层SVG仍具挑战，现有方法或输出过于简单，或产生优化导致的形状冗余。本文提出LayerTracer，一种基于扩散Transformer的框架，通过一个新型的序列化设计操作数据集学习设计师的分层创建过程。方法分为两阶段：首先，文本条件扩散Transformer生成多阶段栅格化构建蓝图，模拟人类设计工作流；其次，分层矢量化结合路径去重，生成干净可编辑的SVG。针对图像矢量化，引入条件扩散机制，将参考图像编码为隐空间令牌，指导分层重建并保持结构完整性。大量实验表明，LayerTracer在生成质量与可编辑性上均优于基于优化和神经网络的基线模型，有效对齐AI生成矢量图与专业设计认知。
原文摘要 · Abstract (English)
Generating cognitive-aligned layered SVGs remains challenging due to existing methods' tendencies toward either oversimplified single-layer outputs or optimization-induced shape redundancies. We propose LayerTracer, a diffusion transformer based framework that bridges this gap by learning designers' layered SVG creation processes from a novel dataset of sequential design operations. Our approach operates in two phases: First, a text-conditioned DiT generates multi-phase rasterized construction blueprints that simulate human design workflows. Second, layer-wise vectorization with path deduplication produces clean, editable SVGs. For image vectorization, we introduce a conditional diffusion mechanism that encodes reference images into latent tokens, guiding hierarchical reconstruction while preserving structural integrity. Extensive experiments demonstrate LayerTracer's superior performance against optimization-based and neural baselines in both generation quality and editability, effectively aligning AI-generated vectors with professional design cognition.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。