Word-As-Image for Semantic Typography
This work addresses the problem of automating semantic typography for designers and artists, representing an incremental improvement by applying existing language-vision models to a specific creative task.
The paper tackles the challenge of automatically generating word-as-image illustrations that visually represent a word's meaning while maintaining readability, achieving high-quality results through optimization guided by a pretrained Stable Diffusion model.
A word-as-image is a semantic typography technique where a word illustration presents a visualization of the meaning of the word, while also preserving its readability. We present a method to create word-as-image illustrations automatically. This task is highly challenging as it requires semantic understanding of the word and a creative idea of where and how to depict these semantics in a visually pleasing and legible manner. We rely on the remarkable ability of recent large pretrained language-vision models to distill textual concepts visually. We target simple, concise, black-and-white designs that convey the semantics clearly. We deliberately do not change the color or texture of the letters and do not use embellishments. Our method optimizes the outline of each letter to convey the desired concept, guided by a pretrained Stable Diffusion model. We incorporate additional loss terms to ensure the legibility of the text and the preservation of the style of the font. We show high quality and engaging results on numerous examples and compare to alternative techniques.