LICA: Layered Image Composition Annotations for Graphic Design Research

arXiv cs.CV / 3/18/2026

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Key Points

LICA introduces a large-scale dataset with 1,550,244 multi-layer graphic design compositions and 971,850 unique templates, plus rendered PNGs and hierarchical per-element data.
Designs are represented as layered structures with typed components (text, image, vector, group) and detailed metadata such as geometry, typography, opacity, and visibility.
The dataset includes 27,261 animated layouts with per-component keyframes and motion parameters, enabling temporally-aware modeling for design tasks.
It proposes new research directions like layer-aware inpainting, structured layout generation, controlled design editing, and temporally-aware generative modeling that focus on design structure rather than pixels.
Spanning 20 design categories, LICA broadens coverage of real-world designs and supports models operating on structure, not just pixel data.

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 layouts1. In addition to ren- dered 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 investiga- tions into problems such as layer-aware inpainting, structured layout generation, controlled design editing, and temporally-aware generative modeling. By repre- senting 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.