LICA: Layered Image Composition Annotations for Graphic Design Research

• URL: http://arxiv.org/abs/2603.16098v2
• Date: Wed, 18 Mar 2026 18:44:02 GMT
• Title: LICA: Layered Image Composition Annotations for Graphic Design Research
• Abstract summary: LICA represents each design as a hierarchical composition of typed components including text, image, vector, and group elements.<n>The dataset spans 20 design categories and 971,850 unique templates, providing broad coverage of real-world design structures.<n>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.
• Score: 7.006851634670039
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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