LayerTracer: Cognitive-Aligned Layered SVG Synthesis via Diffusion Transformer

Yiren Song, Danze_Chen, Mike Shou

LayerTracer: Cognitive-Aligned Layered SVG Synthesis via Diffusion Transformer: 21 upvotes on Hugging Face Daily Papers, #6 of 14 papers on 2025-02-06. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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