WordArt Designer: User-Driven Artistic Typography Synthesis using Large Language Models

This paper introduces WordArt Designer, a user-driven framework for artistic typography synthesis, relying on the Large Language Model (LLM). The system incorporates four key modules: the LLM Engine, SemTypo, StyTypo, and TexTypo modules. 1) The LLM Engine, empowered by the LLM (e.g., GPT-3.5), 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. Experience its capabilities on ModelScope: https://www.modelscope.cn/studios/WordArt/WordArt.Comment: Accepted by EMNLP 2023, 10 pages, 11 figures, 1 table, the system is at https://www.modelscope.cn/studios/WordArt/WordAr

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

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.Comment: Spotlight Paper at the Workshop on Machine Learning for Creativity and Design, 37th Conference on Neural Information Processing Systems (NeurIPS 2023). 5 pages, 5 figure

A Research Funding Allocation Scheme in Multi-Layer Networks for the Growth of Talents

Talent training is a critical issue of social development. Particularly, talent training in research-oriented universities plays a key role in human resources management. However, achieving effective talent development with minimal macro-regulation becomes a challenging problem that has yet to be solved. As an administrator, the allocation of talent project funding is a viable point of focus, although it is difficult to analyze due to the complex structure of the universities. Inspired by the complex networks, we model the academic talent training problem in universities as a multi-layer network in this paper, and the characteristics which may influence the development of faculty are investigated. Then, the development of each scholar is fitted by a growth curve in the life-course pattern, based on which a research funding allocation scheme is proposed from the perspective of human resources managers. In the proposed scheme, the funding quotas of multiple levels are allocated to different colleges at the proper time to obtain the global optimization of talent training for the whole university. The simulation results show that the proposed funding allocation scheme can improve the final academic ability and the normalized score of outstanding scholars compared with those of the traditional proportion-based allocation scheme