LayerTracer: Cognitive-Aligned Layered SVG Synthesis via Diffusion
  Transformer
Authors: name: Yiren Song, name: Danze Chen, name: Mike Zheng Shou

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.