LayerTracer: Cognitive-Aligned Layered SVG Synthesis via Diffusion Transformer

TL;DR

LayerTracer is a diffusion transformer framework that generates layered SVGs aligned with human design cognition by learning from sequential design operations and producing editable, structurally sound vector graphics.

Contribution

It introduces a novel dataset and a two-phase process combining rasterized blueprints and vectorization with path deduplication for improved SVG synthesis.

Findings

Outperforms existing methods in quality and editability

Effectively preserves structural integrity of SVGs

Aligns AI-generated vectors with professional design cognition

Abstract

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.