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<h1 class="title is-1 publication-title"><a style="color:black;">WordArt Designer</a>: <a style="color:black;">User-Driven Artistic Typography Synthesis using Large Language Models</a> </h1>
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<a href="https://scholar.google.com.hk/citations?user=bjNZqGAAAAAJ&hl=en" target="_blank">Jun-Yan He<sup>1</sup><sup>*</sup></a></span>
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<a href="https://scholar.google.com.hk/citations?user=uB2He2UAAAAJ&hl=en" target="_blank"> Zhi-Qi Cheng<sup>2</sup><sup>+</sup></a></span>
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<a href="https://scholar.google.com.hk/citations?user=G08_5nYAAAAJ&hl=en" target="_blank"> Chenyang Li<sup>1</sup> </a></span>
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<a href="" target="_blank"> Jingdong Sun<sup>2</sup></a></span>
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<a href="https://scholar.google.com.hk/citations?user=LFNwNF4AAAAJ&hl=en" target="_blank">Wangmeng Xiang <sup>1</sup></a></span>
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<a href="https://scholar.google.com/citations?user=wLTXeNwAAAAJ&hl=en" target="_blank"> Xianhui Lin<sup>1</sup></a></span>
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<a href="https://scholar.google.com/citations?user=2k54E5wAAAAJ&hl=en" target="_blank"> Xiaoyang Kang<sup>1</sup></a></span>
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<a href="" target="_blank"> Zengke Jin<sup>4</sup></a></span>
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<a href="" target="_blank"> Yusen Hu<sup>3</sup></a></span>
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<a href="" target="_blank"> Bin Luo<sup>1</sup></a></span>
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<a href="" target="_blank"> Yifeng Geng<sup>1</sup></a></span>
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<a href="https://scholar.google.com/citations?user=M0Ei1zkAAAAJ&hl=en" target="_blank"> Xuansong Xie<sup>1</sup></a></span>
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<a href="https://scholar.google.com/citations?user=64zxhRUAAAAJ&hl=en" target="_blank"> Jingren Zhou<sup>1</sup></a></span>
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<span class="author-block"><sup>*</sup>Project Lead, Alibaba Team</span>
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<span class="author-block"><sup>+</sup>Co-Lead, CMU/ICL/RCA Team</span>
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<span class="author-block"><sup>1</sup>DAMO Academy Alibaba Group <sup>2</sup>Carnegie Mellon University </span>
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<span class="author-block"> <sup>3</sup>Imperial College London <sup>4</sup>Royal College of Art </span>
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This paper introduces WordArt Designer, a user-driven framework for artistic typography synthesis, relying on Large Language Models (LLM). The system incorporates four key modules: the LLM Engine, SemTypo, StyTypo, and TexTypo modules. 1) The LLM Engine, empowered by LLM (e.g. GPT-3.5-turbo), interprets user inputs and generates actionable prompts for the other modules, thereby transforming abstract concepts into tangible designs. 2) The SemTypo module optimizes font designs using semantic concepts, striking a balance between artistic transformation and readability. 3) Building on the semantic layout provided by the SemTypo module, the StyTypo module creates smooth, refined images. 4) The TexTypo module further enhances the design's aesthetics through texture rendering, enabling the generation of inventive textured fonts. Notably, WordArt Designer highlights the fusion of generative AI with artistic typography.
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Existing solutions mainly generate semantically coherent and visually pleasing typography within predefined concepts. These solutions often lack adaptability, creativity, and computational efficiency. To overcome these limitations, we introduce WordArt Designer, a system composed of three primary modules: the LLM Engine, SemTypo Module, and StyTypo Module, supplemented by the TexTypo Module for texture rendering.
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<h2 class="title has-text-centered">Framework Overview</h2>
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This structure involves an LLM engine, the SemTypo module for Semantic Typography, the StyTypo module for Stylization Typography, and the TexTypo module for Texture Typography. These modules operate coherently, guided by a preset control flow, to facilitate a seamless and innovative transformation of text into artistic typography.
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<img src="static/images/semtypo-flow.png" width=1024 alt="Method"/>
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The Semantic Typography (SemTypo) module alters typographies based on a given semantic concept. It unfolds in three stages: (1) Character Extraction and Parameterization, (2) Region Selection for Transformation, and (3) Semantic Transformation and Differentiable Rasterization.
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<p id="centerText">Our Surface-Aware Masking algorithm or SAM</p>
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Our Surface-Aware Masking (SAM) algorithm allows us to map unknown 3D areas into 2D accurately. This allows us to then inpaint more accurately which in turn lets us estimate the geometry and texture of the unknown objects in the scene.
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We search for viewpoints by traversing directions along a sphere around the scene away from the original viewpoint. We look for viewpoints using our context ratio (context pixels / all pixels) and use the viewpoint that provides inpainting with enough context to work accurately but still provide new information. We do this for many different directions, and then apply our constistency filtering to obtain our final output.
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<h2 class="title has-text-centered">Qualitative & Quantitative Results</h2>
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We compare our method against 6 state of the art baselines qualitatively and quantitatively. Below are some examples of images generated from our method. For our full analysis please refer to our paper.
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Ablation study of the ranking model on the validation set. `p', `r', and `s' stand for precision, recall, and success rate, respectively. `x' in `TopX' indicates the number of stylized images retained. In the ranking-based method, `TopX' are selected based on ranking scores, while for the random-based method, `TopX' are selected randomly. Results of the random-based method are obtained by averaging over 10,000 iterations. Increased values are indicated in blue.
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<h2 class="title is-3">Abstract</h2>
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Our method also has the capability to produce realistic novel views of multiple objects. Using the Rotation + SAM + Inpaint parts of our method, we show qualitative results of our methods ability to generate novel views given an RGB-D image.
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<pre><code>@misc{he2023wordart,
title = {WordArt Designer: User-Driven Artistic Typography Synthesis using Large Language Models},
author = {Jun-Yan He Zhi-Qi Cheng Chenyang Li Jingdong Sun Wangmeng Xiang Xianhui Lin Xiaoyang Kang Zengke Jin Yusen Hu Bin Luo Yifeng Geng Xuansong Xie Jingren Zhou},
year={2023},
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archivePrefix={arXiv},
primaryClass={cs.CV}
}
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