LogoMotion: Visually Grounded Code Generation for Content-Aware
  Animation
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.
6642e89e3cf4dc637fcbff7c
Vivian Liu
admin_assigned
2024-05-14T08:45:41.905Z
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/avatars/f1d79fdb39578d07d43470eeb6b01545.svg
Vivian Liu
user
vivlavida
6642e89e3cf4dc637fcbff7d
Rubaiat Habib Kazi
6642e89e3cf4dc637fcbff7e
Li-Yi Wei
admin_assigned
2024-05-14T08:45:54.107Z
6319292bc3fc97d443a8a5f9
https://cdn-avatars.huggingface.co/v1/production/uploads/1662593317718-noauth.png
Li-Yi Wei
user
liyiwei
6642e89e3cf4dc637fcbff7f
Matthew Fisher
6642e89e3cf4dc637fcbff80
Timothy Langlois
extracted_confirmed
2024-05-14T17:23:41.540Z
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/avatars/ea634fd8b4a80a94e3f55fed03dc15a1.svg
Tim Langlois
user
tlanglois
6642e89e3cf4dc637fcbff81
Seth Walker
6642e89e3cf4dc637fcbff82
Lydia Chilton
admin_assigned
2024-05-14T08:47:06.236Z
649ae3c000f8bbeefb84161f
/avatars/f1f769201734aa297c4fe923a8c8e8cd.svg
Lydia Chilton
user
lydiachilton
LogoMotion: Visually Grounded Code Generation for Content-Aware
  Animation
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.
LogoMotion is an LLM-based system that generates animation code for logos, producing visually-grounded and content-aware animations.
LLM-based system visually-grounded program synthesis HTML representation primary and secondary elements animation code animation errors visually debug