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LayerTracer: Cognitive-Aligned Layered SVG Synthesis via Diffusion Transformer

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arxiv 2502.01105 v3 pith:H7ZHPPWS submitted 2025-02-03 cs.CV

Yiren Song , Danze Chen , Mike Zheng Shou This is my paper · ORCID
classification cs.CV
keywords designdiffusionlayeredlayertracercognitive-alignedsvgstransformervectorization
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ReMoT: Reinforcement Learning with Motion Contrast Triplets

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    Training a 4B vision-language model on rule-generated motion-contrast triplets with GRPO lifts spatio-temporal QA accuracy by about 17 points on the authors' own benchmark and by smaller margins on standard benchmarks.

  2. GUIPilot: A Consistency-based Mobile GUI Testing Approach for Detecting Application-specific Bugs

    cs.SE 2025-06 conditional novelty 6.0 of 10

    GUIPilot compares mobile app screens and workflows against design mock-ups, using widget sequence alignment and a vision-language model to detect layout and transition inconsistencies.

  3. OmniConsistency: Learning Style-Agnostic Consistency from Paired Stylization Data

    cs.CV 2025-05 conditional novelty 6.0 of 10

    OmniConsistency is a style-agnostic consistency module for Flux that preserves structure and details during stylization with arbitrary LoRAs, reaching GPT-4o-level content consistency.

  4. RelationAdapter: Learning and Transferring Visual Relation with Diffusion Transformers

    cs.CV 2025-06 conditional novelty 5.0 of 10

    A decoupled-attention adapter transfers image-pair edits to new photos in diffusion transformers, trained with a new 218-task visual editing dataset.

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