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Welcome to another exciting episode of the Talking Papers Podcast! In this installment, I had the pleasure of hosting Shir Iluz to discuss her groundbreaking paper titled "Word-As-Image for Semantic Typography" which won the SIGGRAPH 2023 Honorable Mention award.
This scientific paper introduces an innovative approach for text morphing based on semantic context. Using bezier curves with control points, a rasterizer, and a vector diffusion model, the authors transform words like "bunny" into captivating bunny-shaped letters. Their optimization-based method accurately conveys the word's meaning. They address the readability-semantic balance with multiple loss functions, serving as "control knobs" for users to fine-tune results. The paper's compelling results are showcased in an impressive demo. Don't miss it!
Their work carries immense potential, promising to revolutionize the creative processes of artists and designers. Rather than commencing from a traditional blank canvas or plain font, this innovative approach enables individuals to initiate their logo design journey by transforming a word into a captivating image. The implications of this novel technique hold the power to reshape the very workflow of artistic expression, opening up exciting new possibilities for visual communication and design aesthetics.
I am eagerly anticipating the next set of papers she will sketch out (pun intended).
AUTHORS
Shir Iluz, Yael Vinker, Amir Hertz, Daniel Berio, Daniel Cohen-Or, Ariel Shamir
ABSTRACT
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.
RELATED PAPERS
📚VectorFusion
LINKS AND RESOURCES
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💻 Project page
💻 Code
💻 Demo
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