VisualFusion: Enhancing Blog Content with Advanced Infographic Pipeline

Anurag Deo, Savita Bhat, Shirish S. Karande

Abstract: Infographics represent a key component of any blog or article, facilitating effective communication of ideas while fos-tering reader engagement. However, many content creators possess limited expertise in crafting visually striking info-graphics. This gap is effectively addressed by our proposed pipeline, designed to aid writers in generating compelling infographics tailored to their written content. Our pipeline uses textual content and tabular data from the blog to gen-erate anchor plots. Leveraging LLM for prompt generation, the pipeline integrates the generated prompts with these anchor plots through a Image to Image (121) generation Model. We observe that majority of resulting images generated using this approach align with the article's narrative and effectively represent the underlying tabular data. Additionally, we introduce our proposed AADaT (Aesthetical Adherence to Data and Text) Score, adept at comprehen-sively assessing aesthetics, textual alignment, data fidelity, and overall image quality concurrently. In comparative evaluations, our pipeline has demonstrated around 15% superior performance relative to state-of-the-art models such as DALL-e and Stable Diffusion Large by showcasing much better data adherence and aesthetics. While state-of-the-art models excel in some metrics but falter in others, our pipeline demonstrates a balanced performance across all metrics. The source code and data corpus may be available on request.

External IDs:dblp:conf/wacv/DeoBK25