LICA: Layered Image Composition Annotations for Graphic Design Research
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Abstract (arXiv)
We introduce LICA (Layered Image Composition Annotations), a large scale dataset of 1,550,244 multi-layer graphic design compositions designed to advance structured understanding and generation of graphic layouts. In addition to rendered PNG images, LICA represents each design as a hierarchical composition of typed components including text, image, vector, and group elements, each paired with rich per-element metadata such as spatial geometry, typographic attributes, opacity, and visibility. The dataset spans 20 design categories and 971,850 unique templates, providing broad coverage of real-world design structures. We further introduce graphic design video as a new and largely unexplored challenge for current vision-language models through 27,261 animated layouts annotated with per-component keyframes and motion parameters. Beyond scale, LICA establishes a new paradigm of research tasks for graphic design, enabling structured investigations into problems such as layer-aware inpainting, structured layout generation, controlled design editing, and temporally-aware generative modeling. By representing design as a system of compositional layers and relationships, the dataset supports research on models that operate directly on design structure rather than pixels alone.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 2 of its 4 references that are in the map. First posted on arXiv 17 Mar 2026.
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2 of 4 references.
References · 2
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Citing papers per year: 2026: 6.
6 papers.
- 2026Structural Evaluation Metrics for SVG Generation via Leave-One-Out Analysiscited by 1
- 2026Does Synthetic Layered Design Data Benefit Layered Design Decomposition?cited by 1
- 2026Detect Anything in Graphic Design: Element-Level Rewards for Autoregressive Detectioncited by 1
- 2026Evaluating Design Video Generation: Metrics for Compositional Fidelitycited by 0
- 2026TASTE: A Designer-Annotated Multi-Dimensional Preference Dataset for AI-Generated Graphic Designcited by 0
- 2026PixelART: Image-to-Layer Decomposition without Latents or Text-to-Image Pretrainingcited by 0
- 2026PixelART: Image-to-Layer Decomposition without Latents or Text-to-Image Pretraining19 Sep 2026
- 2026Detect Anything in Graphic Design: Element-Level Rewards for Autoregressive Detection7 Sep 2026
- 2026TASTE: A Designer-Annotated Multi-Dimensional Preference Dataset for AI-Generated Graphic Design20 May 2026
- 2026Evaluating Design Video Generation: Metrics for Compositional Fidelity15 May 2026
- 2026Does Synthetic Layered Design Data Benefit Layered Design Decomposition?14 May 2026
- 2026Structural Evaluation Metrics for SVG Generation via Leave-One-Out Analysis9 Apr 2026
Similar, not cited
Close in wording, no citation either way
- 2024From Elements to Design: A Layered Approach for Automatic Graphic Design Composition
- 2026Graphic-Design-Bench: A Comprehensive Benchmark for Evaluating AI on Graphic Design Tasks
- 2026Beyond Atomic Layouts: Compositional Design Understanding with Vision-Language Models
- 2024Graphic Design with Large Multimodal Model
- 2026Learning Interaction between Image and Layout Priors for Joint Image-Layout Generation in Design Templates
- 2026PosterIQ: A Design Perspective Benchmark for Poster Understanding and Generation
- 2024DesignProbe: A Graphic Design Benchmark for Multimodal Large Language Models
- 2026CreatiParser: Generative Image Parsing of Raster Graphic Designs into Editable Layers