# LICA: Layered Image Composition Annotations for Graphic Design Research

arXiv: https://arxiv.org/abs/2603.16098 · HTML: https://mlsanity.com/paper/2603.16098/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 4 references that are in the map.
- First posted on arXiv 17 Mar 2026.

## Builds on (most cited first, 2 of 2)

- [CanvasVAE: Learning to Generate Vector Graphic Documents](https://mlsanity.com/paper/2108.01249/) (2021)
- [PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation Layout](https://mlsanity.com/paper/2303.15937/) (2023)

## Built on by (most cited first, 7 of 7)

- [Graphic-Design-Bench: A Comprehensive Benchmark for Evaluating AI on Graphic Design Tasks](https://mlsanity.com/paper/2604.04192/) (2026)
- [Structural Evaluation Metrics for SVG Generation via Leave-One-Out Analysis](https://mlsanity.com/paper/2604.08809/) (2026)
- [Does Synthetic Layered Design Data Benefit Layered Design Decomposition?](https://mlsanity.com/paper/2605.15167/) (2026)
- [Detect Anything in Graphic Design: Element-Level Rewards for Autoregressive Detection](https://mlsanity.com/paper/2609.07072/) (2026)
- [Evaluating Design Video Generation: Metrics for Compositional Fidelity](https://mlsanity.com/paper/2605.16223/) (2026)
- [TASTE: A Designer-Annotated Multi-Dimensional Preference Dataset for AI-Generated Graphic Design](https://mlsanity.com/paper/2605.20731/) (2026)
- [PixelART: Image-to-Layer Decomposition without Latents or Text-to-Image Pretraining](https://mlsanity.com/paper/2609.22789/) (2026)

## 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.