title: LayerTracer: Cognitive-Aligned Layered SVG Synthesis via Diffusion
    Transformer
summary: 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_authors._id: 67a45c85e73ad243c0b9529e
paper_authors.name: Yiren Song
paper_authors._id: 67a45c85e73ad243c0b9529f
paper_authors.name: Danze Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-07T09:58:20.740Z
paper_authors._id: 6729d1fed3ec5370cb035901
paper_authors.avatarUrl: /avatars/50f7ce9c635148df76d1c63ebf3efa38.svg
paper_authors.fullname: 1
paper_authors.type: user
paper_authors.user: DANNY621
paper_authors._id: 67a45c85e73ad243c0b952a0
paper_authors.name: Mike Zheng Shou
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-02-06T06:54:02.195Z
paper_authors._id: 63a55320ce5763e06f78519c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1671779060549-noauth.jpeg
paper_authors.fullname: Mike Shou
paper_authors.type: user
paper_authors.user: mikeshou
paper_title: LayerTracer: Cognitive-Aligned Layered SVG Synthesis via Diffusion
    Transformer
paper_summary: 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_ai_keywords: diffusion transformer multi-phase rasterized construction blueprints conditional diffusion text-conditioned DiT latent tokens conditional diffusion mechanism path deduplication hierarchical reconstruction structural integrity LayerTracer generation quality editability neural baselines AI-generated vectors professional design cognition