用自然语言指令生成可变形显示屏的动态形状变化，无需编程。
SHAPE-IT: Exploring Text-to-Shape-Display for Generative Shape-Changing Behaviors with LLMs
- 通过大模型和AI链技术，将文本指令转化为可执行代码。
- 在24×24针式显示屏上实现快速原型，支持交互式探索。
- 适合设计师与非技术人员快速试错，但存在生成精度挑战。
本文提出文本到形状显示（text-to-shape-display）的新方法，利用大语言模型（LLMs）与AI链技术，通过自然语言命令在针式可变形显示屏上生成动态形状变化。该方法无需编程，支持用户按需创作形变行为。研究基于前期探索与迭代设计，识别出生成核心要素（基础形态、动画、交互）及设计需求。据此开发了名为SHAPE-IT的基于LLM的创作工具，适用于24×24针式显示屏，能将文本指令转为可执行代码，并通过网页界面实现快速探索。评估包括性能测试与用户实验（N=10），结果表明该系统有效促进多样化形变行为的快速构思，但亦暴露出生成准确性的挑战，亟需进一步优化框架以适配可变形系统的特殊需求。
原文摘要 · Abstract (English)
This paper introduces text-to-shape-display, a novel approach to generating dynamic shape changes in pin-based shape displays through natural language commands. By leveraging large language models (LLMs) and AI-chaining, our approach allows users to author shape-changing behaviors on demand through text prompts without programming. We describe the foundational aspects necessary for such a system, including the identification of key generative elements (primitive, animation, and interaction) and design requirements to enhance user interaction, based on formative exploration and iterative design processes. Based on these insights, we develop SHAPE-IT, an LLM-based authoring tool for a 24 x 24 shape display, which translates the user's textual command into executable code and allows for quick exploration through a web-based control interface. We evaluate the effectiveness of SHAPE-IT in two ways: 1) performance evaluation and 2) user evaluation (N= 10). The study conclusions highlight the ability to facilitate rapid ideation of a wide range of shape-changing behaviors with AI. However, the findings also expose accuracy-related challenges and limitations, prompting further exploration into refining the framework for leveraging AI to better suit the unique requirements of shape-changing systems.
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