---
title: 'LICA Dataset: Layered-Composition for Design'
url: https://www.emergentmind.com/topics/lica-layered-composition-dataset
type: topic
---

# LICA Dataset: Layered-Composition for Design

The LICA (Layered Image Composition Annotations) Layered-Composition Dataset is a large-scale, richly annotated corpus of professional graphic designs structured as hierarchical, multi-element compositions. Encompassing over 1.5 million layouts across diverse design categories, LICA formalizes each design as a system of typed components—text, images, vectors, and groups—preserved with extensive per-element metadata. The dataset supports robust research in automated layout understanding, generative models for design, temporally-aware graphic composition, and structured editing, marking a substantial advancement over pixel-based datasets. Each composition is provided as rendered PNGs and in a detailed JSON schema that reflects both the graphical output and the underlying structural specification, with a particular emphasis on explicitly modeling design semantics and hierarchical relationships [2603.16098].

## 1. Dataset Scope and Composition Diversity

LICA comprises 1,550,244 discrete compositions ("layouts"), gathered to maximize structural and stylistic diversity as encountered in real-world graphic design workflows. Of these, 971,850 represent unique design templates, with 107,728 templates offering 2–24 style and content variants. This enables direct investigation into style consistency, template-conditioned generalization, and controlled design variation.

The dataset spans 20 professional sub-categories, with the majority of layouts reflecting high-volume social and business use-cases:

| Category         | Layouts    | Percentage      |
|------------------|------------|-----------------|
| Instagram Post   | 599,758    | 38.7%           |
| Presentation     | 448,623    | 28.9%           |
| Education        | 125,008    | 8.1%            |
| Flyer            | 74,308     | 4.8%            |
| Social Media     | 52,850     | 3.4%            |
| Art Design       | 2,649      | 0.2%            |

The full range encompasses infographics, business cards, resumes, menus, posters, video advertisements, and more, establishing broad representativeness for layout and compositional research. The dataset does not specify a canonical train/validation/test split, encouraging user-defined strategies such as reserving entire templates as a held-out set to rigorously assess model generalization [2603.16098].

## 2. Annotation Schema and Data Model

LICA structures each layout as a hierarchical composition \( C = \{e_1, e_2, \ldots, e_n\} \), with each element \( e_i \) typed as text, image, vector (SVG or Lottie JSON), or group (including frame grids). Complex layouts are recursively decomposed into nested groups:
\[
C = G_1 \cup G_2 \cup \cdots \cup G_K, \quad G_j = \{e_{j1}, e_{j2}, \ldots, e_{j,m_j} \}
\]
allowing for arbitrarily deep trees. While the released JSON flatten this hierarchy, parent indices and transform chains recover the original nesting.

Per-element metadata includes:

- **Spatial geometry**: \( x_i, y_i \) (absolute top-left), \( w_i, h_i \) (width, height)
- **Transform chain**: CSS-style encoding (rotation \( \theta_i \), scale \( s_{x_i}, s_{y_i} \), flips)
- **Opacity**: \( \alpha_i \in [0,1] \)
- **Visibility**: flag \( v_i \in \{true, false\} \)
- **Layer order**: \( z_i \in \mathbb{N} \), governing stacking

Type-specific fields capture domain detail. For example, text components (8,146,222 total) preserve UTF-8 content, font family, size, weight, color, line height, letter spacing, text alignment, curvature (for arc text), auto-resize behavior, per-span style overrides, and (optionally) SVG background textures. Image (7,019,837) and vector (4,196,047, of which 619 are Lottie animations) elements retain original asset properties, clipping/cropping, overlays, and alternative text. Group components (5,315,918) encode child lists, group geometry, internal transforms, overflow, clip-paths, and frame-grid layout parameters for tiled arrangements.

## 3. Animated Layouts and Temporal Semantics

A distinctive aspect of LICA is its inclusion of 27,261 animated or "video" layouts. For each element \( e_i \) in a video composition, the temporal annotation \( M(e_i) \) specifies:

\[
M(e_i) = \{ K_i = \{ (t_k, props_k) \}, E_i, D_i, t_{0i} \}
\]

where \( K_i \) collects keyframes (timestamped property sets), \( E_i \) is an easing curve per segment, \( D_i \) the per-element duration, and \( t_{0i} \) the start-time offset. Motion is categorized into 32 distinct types (e.g., slide-in, fade, scale, etc.), and temporal interpolation leverages CSS-standard easing (linear, ease-in, cubic-Bezier). For multi-slide animated designs, 11 inter-slide transitions are defined, each parameterized by direction and color. This temporal structuring enables research on generative video layouts and sequence modeling in design contexts [2603.16098].

## 4. Research Tasks and Benchmark Applications

LICA is structured to support and catalyze a spectrum of graphic design research problems:

- **Structured layout generation**: Predicting full component sets—including types, positions, visual styles, and layer order—conditioned on natural-language briefs or partial sketches.
- **Constraint-preserving design editing (layer-aware inpainting)**: Training models to remove or substitute specific elements while maintaining coherent, plausible compositions.
- **Controlled template variation**: Utilizing template-variant groupings to learn consistency in style with content or palette swaps.
- **Temporally-aware generation**: Modeling and generating synchronized motion for compositional elements to produce animated layouts or story ads.
- **Font classification and typographic synthesis**: Generating per-character styling, font family/weight prediction, and curved text arrangement.
- **Design quality evaluation and ranking**: Learning metrics that combine geometry, typographic hierarchy, and template-conditional appropriateness.

At release, LICA proposes these new research tasks but does not yet provide quantitative baselines. A pilot subset comprising 1,000 layouts (including all data types and categories) is distributed for early benchmarking and comparative analysis [2603.16098].

## 5. Access, Contents, and Licensing

LICA is distributed through https://github.com/purvanshi-lica/lica-dataset. Release artifacts include PNG previews (for rendered appearance), hierarchical JSONs with full annotation for each layout, and the 1K-layout pilot benchmark subset. Licensing follows a non-commercial, Creative Commons-style agreement; detailed terms and correct citation form are documented in the repository. Proper citation is required for research use:  
Hirsch E., Yadav S., Garg M., Mehta P. “LICA: Layered Image Composition Annotations for Graphic Design Research,” 2024 [2603.16098].

## 6. Significance and Paradigm Shift in Graphic Design Datasets

LICA’s explicit modeling of layered composition, typographic detail, vector elements, and animation semantics marks a substantial departure from prior, pixel-centric graphics benchmarks. By encoding the structural and relational complexity of real-world designs, LICA encourages the development of models operating directly on compositional and semantic primitives rather than on rasterized appearance alone. This suggests a shift toward generative and interpretive systems with controllable, editable, and semantically grounded outputs. Its organization is tailored to foster novel directions in structured layout synthesis, layer-aware editing, temporal generation, and holistic design evaluation, potentially underpinning the next generation of vision-language and design-centric machine learning architectures [2603.16098].

Source: https://www.emergentmind.com/topics/lica-layered-composition-dataset