WordArt Designer API: User-Driven Artistic Typography Synthesis with Large Language Models on ModelScope

• URL: http://arxiv.org/abs/2401.01699v2
• Date: Fri, 12 Jan 2024 22:09:09 GMT
• Title: WordArt Designer API: User-Driven Artistic Typography Synthesis with Large Language Models on ModelScope
• Authors: Jun-Yan He, Zhi-Qi Cheng, Chenyang Li, Jingdong Sun, Wangmeng Xiang, Yusen Hu, Xianhui Lin, Xiaoyang Kang, Zengke Jin, Bin Luo, Yifeng Geng, Xuansong Xie, Jingren Zhou
• Abstract summary: This paper introduces the WordArt Designer API, a novel framework for user-driven artistic typography synthesis utilizing Large Language Models (LLMs) on ModelScope. We address the challenge of simplifying artistic typography for non-professionals by offering a dynamic, adaptive, and computationally efficient alternative to traditional rigid templates.
• Score: 43.68826200853858
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces the WordArt Designer API, a novel framework for user-driven artistic typography synthesis utilizing Large Language Models (LLMs) on ModelScope. We address the challenge of simplifying artistic typography for non-professionals by offering a dynamic, adaptive, and computationally efficient alternative to traditional rigid templates. Our approach leverages the power of LLMs to understand and interpret user input, facilitating a more intuitive design process. We demonstrate through various case studies how users can articulate their aesthetic preferences and functional requirements, which the system then translates into unique and creative typographic designs. Our evaluations indicate significant improvements in user satisfaction, design flexibility, and creative expression over existing systems. The WordArt Designer API not only democratizes the art of typography but also opens up new possibilities for personalized digital communication and design.

Related papers

• GLDesigner: Leveraging Multi-Modal LLMs as Designer for Enhanced Aesthetic Text Glyph Layouts [53.568057283934714]
  We propose a VLM-based framework that generates content-aware text logo layouts. We introduce two model techniques to reduce the computation for processing multiple glyph images simultaneously. To support instruction-tuning of out model, we construct two extensive text logo datasets, which are 5x more larger than the existing public dataset.
  arXiv  Detail & Related papers  (2024-11-18T10:04:10Z)
• Computational Modeling of Artistic Inspiration: A Framework for Predicting Aesthetic Preferences in Lyrical Lines Using Linguistic and Stylistic Features [8.205321096201095]
  Artistic inspiration plays a crucial role in producing works that resonate deeply with audiences. This work proposes a novel framework for computationally modeling artistic preferences in different individuals. Our framework outperforms an out-of-the-box LLaMA-3-70b, a state-of-the-art open-source language model, by nearly 18 points.
  arXiv  Detail & Related papers  (2024-10-03T18:10:16Z)
• MetaDesigner: Advancing Artistic Typography through AI-Driven, User-Centric, and Multilingual WordArt Synthesis [65.78359025027457]
  MetaDesigner revolutionizes artistic typography by leveraging the strengths of Large Language Models (LLMs) to drive a design paradigm centered around user engagement. A comprehensive feedback mechanism harnesses insights from multimodal models and user evaluations to refine and enhance the design process iteratively. Empirical validations highlight MetaDesigner's capability to effectively serve diverse WordArt applications, consistently producing aesthetically appealing and context-sensitive results.
  arXiv  Detail & Related papers  (2024-06-28T11:58:26Z)
• Stylebreeder: Exploring and Democratizing Artistic Styles through Text-to-Image Models [38.5896770458884]
  We introduce textttSTYLEBREEDER, a comprehensive dataset of 6.8M images and 1.8M prompts generated by 95K users on Artbreeder. By documenting unique, user-generated styles that transcend conventional categories like 'cyberpunk' or 'Picasso,' we explore the potential for unique, crowd-sourced styles. Our research demonstrates the potential of text-to-image diffusion models to uncover and promote unique artistic expressions.
  arXiv  Detail & Related papers  (2024-06-20T17:59:56Z)
• Choreographing the Digital Canvas: A Machine Learning Approach to Artistic Performance [9.218587190403174]
  This paper introduces the concept of a design tool for artistic performances based on attribute descriptions. The platform integrates a novel machine-learning (ML) model with an interactive interface to generate and visualize artistic movements.
  arXiv  Detail & Related papers  (2024-03-26T01:42:13Z)
• CreativeSynth: Creative Blending and Synthesis of Visual Arts based on Multimodal Diffusion [74.44273919041912]
  Large-scale text-to-image generative models have made impressive strides, showcasing their ability to synthesize a vast array of high-quality images. However, adapting these models for artistic image editing presents two significant challenges. We build the innovative unified framework Creative Synth, which is based on a diffusion model with the ability to coordinate multimodal inputs.
  arXiv  Detail & Related papers  (2024-01-25T10:42:09Z)
• WordArt Designer: User-Driven Artistic Typography Synthesis using Large Language Models [43.68826200853858]
  This paper introduces WordArt Designer, a user-driven framework for artistic typography synthesis. The system incorporates four key modules: the LLM Engine, SemTypo, StyTypo, and TexTypo modules. Notably, WordArt Designer highlights the fusion of generative AI with artistic typography.
  arXiv  Detail & Related papers  (2023-10-20T12:44:44Z)
• TEMOS: Generating diverse human motions from textual descriptions [53.85978336198444]
  We address the problem of generating diverse 3D human motions from textual descriptions. We propose TEMOS, a text-conditioned generative model leveraging variational autoencoder (VAE) training with human motion data. We show that TEMOS framework can produce both skeleton-based animations as in prior work, as well more expressive SMPL body motions.
  arXiv  Detail & Related papers  (2022-04-25T14:53:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.