LogoMotion: Visually Grounded Code Generation for Content-Aware Animation

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

LogoMotion: Visually Grounded Code Generation for Content-Aware Animation: 18 upvotes on Hugging Face Daily Papers, #5 of 9 papers on 2024-05-14. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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