LayerTracer: Cognitive-Aligned Layered SVG Synthesis via Diffusion
  Transformer
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.
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Yiren Song
67a45c85e73ad243c0b9529f
Danze Chen
claimed_verified
2025-02-07T09:58:20.740Z
6729d1fed3ec5370cb035901
/avatars/50f7ce9c635148df76d1c63ebf3efa38.svg
1
user
DANNY621
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Mike Zheng Shou
extracted_pending
2025-02-06T06:54:02.195Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1671779060549-noauth.jpeg
Mike Shou
user
mikeshou
LayerTracer: Cognitive-Aligned Layered SVG Synthesis via Diffusion
  Transformer
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.
LayerTracer, a diffusion transformer framework, generates high-quality, editable layered SVGs using text-conditioned DiT for blueprint generation and conditional diffusion for vectorization, outperforming existing methods in quality and editability.
diffusion transformer LayerTracer DiT multi-phase rasterized construction text-conditioned layer-wise vectorization path deduplication conditional diffusion latent tokens hierarchical reconstruction structural integrity