3 posts tagged with "Automatic"

TLDR

The study investigates the involvement of automation in narrative visualization tools, summarizing six genres: annotated charts, infographics, timelines & storylines, data comics, scrollytelling & slideshow, and data videos. It categorizes four types of tools:design spaces, authoring tools, ML/AI-supported tools, and ML/AI-generator tools according to their intelligence and automation levels. By surveying 105 papers and tools, the study explores how automation can progressively engage in visualization design and narrative processes to aid users in creating narrative visualizations. It aims to provide an overview of current research and development, identifies key research problems in each category, and suggests opportunities for further investigation in this domain.

TLDR

LIDA is a novel tool for generating grammar-agnostic visualizations and infographics. It addresses several key challenges in automatic visualization creation:

1. Understanding data semantics
2. Enumerating visualization goals
3. Generating visualization specifications

The system uses a multi-stage pipeline powered by large language models (LLMs) and image generation models (IGMs). It consists of 4 main modules:

1. SUMMARIZER: Converts data into compact natural language summaries
2. GOAL EXPLORER: Identifies potential visualization goals based on the data
3. VISGENERATOR: Handles visualization code generation, refinement and filtering
4. INFOGRAPHER: Creates data-driven stylized graphics using IGMs

LIDA provides both a Python API and an interactive user interface supporting direct manipulation and multilingual natural language for generating charts, infographics and data stories.

TLDR

In this article, I will share my thoughts on the automatic generation of designer materials based on my experience working on a Design Platform project. I'll discuss how we implemented a semi-automated system for creating marketing visuals, the principles behind it, and explore the broader implications for design automation. This approach demonstrates how combining designer-provided frameworks with rule-based generation can create scalable design solutions while maintaining quality standards.