Textured Word-As-Image illustration
Mohammad Javadian Farzaneh, Selim Balcisoy

TL;DR
This paper introduces an automatic pipeline that generates textured text images aligned with semantic concepts, allowing real-time customization while maintaining readability, useful for design and artistic purposes.
Contribution
It presents a novel method combining semantic input, texture generation via stable diffusion, and real-time adjustments to produce legible, semantically aligned textured text images.
Findings
Effective semantic-texture alignment demonstrated
High user satisfaction in readability and customization
Real-time adjustment feature enhances usability
Abstract
In this paper, we propose a novel fully automatic pipeline to generate text images that are legible and strongly aligned to the desired semantic concept taken from the users' inputs. In our method, users are able to put three inputs into the system, including a semantic concept, a word, and a letter. The semantic concept will be used to change the shape of the input letter and generate the texture based on the pre-defined prompt using stable diffusion models. Our pipeline maps the texture on a text image in a way that preserves the readability of the whole output while preserving legibility. The system also provides real-time adjustments for the user to change the scale of the texture and apply it to the text image. User evaluations demonstrate that our method effectively represents semantic meaning without compromising legibility, making it a robust and innovative tool for graphic…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsInteractive and Immersive Displays · Generative Adversarial Networks and Image Synthesis · Computer Graphics and Visualization Techniques
