English

Neural Contrast: Leveraging Generative Editing for Graphic Design Recommendations

Computer Vision and Pattern Recognition 2024-10-11 v1 Graphics Human-Computer Interaction Machine Learning

Abstract

Creating visually appealing composites requires optimizing both text and background for compatibility. Previous methods have focused on simple design strategies, such as changing text color or adding background shapes for contrast. These approaches are often destructive, altering text color or partially obstructing the background image. Another method involves placing design elements in non-salient and contrasting regions, but this isn't always effective, especially with patterned backgrounds. To address these challenges, we propose a generative approach using a diffusion model. This method ensures the altered regions beneath design assets exhibit low saliency while enhancing contrast, thereby improving the visibility of the design asset.

Keywords

Cite

@article{arxiv.2410.07211,
  title  = {Neural Contrast: Leveraging Generative Editing for Graphic Design Recommendations},
  author = {Marian Lupascu and Ionut Mironica and Mihai-Sorin Stupariu},
  journal= {arXiv preprint arXiv:2410.07211},
  year   = {2024}
}

Comments

14 pages, 5 figures, Paper sent and accepted as a poster at PRICAI 2024

R2 v1 2026-06-28T19:14:57.941Z