LICA: Layered Image Composition Annotations for Graphic Design Research
This provides a foundational resource for graphic design research, enabling models to operate on design structure rather than pixels, though it is incremental in creating a new dataset rather than a novel method.
The authors tackled the problem of advancing structured understanding and generation of graphic layouts by introducing LICA, a large-scale dataset of 1,550,244 multi-layer graphic design compositions, which enables new research tasks like layer-aware inpainting and structured layout generation.
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.