LogoMotion：視覺基礎的程式碼生成，用於內容感知動畫

LogoMotion: Visually Grounded Code Generation for Content-Aware Animation

May 11, 2024

作者: Vivian Liu, Rubaiat Habib Kazi, Li-Yi Wei, Matthew Fisher, Timothy Langlois, Seth Walker, Lydia Chilton

cs.AI

摘要

動畫標誌是個人和品牌在線上展示自己的一種引人注目且普遍的方式。手動製作這些標誌可能需要相當的藝術技巧和努力。為了幫助新手設計師製作動畫標誌，設計工具目前提供模板和動畫預設。然而，這些解決方案在表現範圍上可能有限。大型語言模型有潛力幫助新手設計師通過生成適合其內容的動畫代碼來創建動畫標誌。在本文中，我們介紹了一個名為LogoMotion的基於LLM的系統，該系統接受分層文檔並通過視覺基礎的程序合成生成動畫標誌。我們介紹了創建畫布的HTML表示、識別主要和次要元素、合成動畫代碼以及視覺調試動畫錯誤的技術。與行業標準工具相比，我們發現LogoMotion生成的動畫更具內容意識，並在質量方面不相上下。最後，我們討論了LLM生成動畫對運動設計的影響。

English

Animated logos are a compelling and ubiquitous way individuals and brands represent themselves online. Manually authoring these logos can require significant artistic skill and effort. To help novice designers animate logos, design tools currently offer templates and animation presets. However, these solutions can be limited in their expressive range. Large language models have the potential to help novice designers create animated logos by generating animation code that is tailored to their content. In this paper, we introduce LogoMotion, an LLM-based system that takes in a layered document and generates animated logos through visually-grounded program synthesis. We introduce techniques to create an HTML representation of a canvas, identify primary and secondary elements, synthesize animation code, and visually debug animation errors. When compared with an industry standard tool, we find that LogoMotion produces animations that are more content-aware and are on par in terms of quality. We conclude with a discussion of the implications of LLM-generated animation for motion design.