LICA: Layered Image Composition Annotations for Graphic Design Research

TL;DR

LICA is a comprehensive large-scale dataset of layered graphic design compositions with rich annotations, enabling advanced research in structured graphic layout understanding and generation, including static and animated designs.

Contribution

The paper introduces LICA, a large-scale, richly annotated dataset of layered graphic designs and animated layouts, facilitating new research directions in graphic design AI.

Findings

Provides 1.55 million multi-layer compositions across 20 categories.

Includes 27,261 animated layouts with motion annotations.

Supports new tasks like layer-aware inpainting and structured layout generation.

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.