title: LogoMotion: Visually Grounded Code Generation for Content-Aware
    Animation
summary: 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_authors._id: 6642e89e3cf4dc637fcbff7c
paper_authors.name: Vivian Liu
paper_authors.status: admin_assigned
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paper_authors.fullname: Vivian Liu
paper_authors.type: user
paper_authors.user: vivlavida
paper_authors._id: 6642e89e3cf4dc637fcbff7d
paper_authors.name: Rubaiat Habib Kazi
paper_authors._id: 6642e89e3cf4dc637fcbff7e
paper_authors.name: Li-Yi Wei
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-14T08:45:54.107Z
paper_authors._id: 6319292bc3fc97d443a8a5f9
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paper_authors.fullname: Li-Yi Wei
paper_authors.type: user
paper_authors.user: liyiwei
paper_authors._id: 6642e89e3cf4dc637fcbff7f
paper_authors.name: Matthew Fisher
paper_authors._id: 6642e89e3cf4dc637fcbff80
paper_authors.name: Timothy Langlois
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-05-14T17:23:41.540Z
paper_authors._id: 634110e6388c3fa40fa16a8f
paper_authors.avatarUrl: /avatars/ea634fd8b4a80a94e3f55fed03dc15a1.svg
paper_authors.fullname: Tim Langlois
paper_authors.type: user
paper_authors.user: tlanglois
paper_authors._id: 6642e89e3cf4dc637fcbff81
paper_authors.name: Seth Walker
paper_authors._id: 6642e89e3cf4dc637fcbff82
paper_authors.name: Lydia Chilton
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-14T08:47:06.236Z
paper_authors._id: 649ae3c000f8bbeefb84161f
paper_authors.avatarUrl: /avatars/f1f769201734aa297c4fe923a8c8e8cd.svg
paper_authors.fullname: Lydia Chilton
paper_authors.type: user
paper_authors.user: lydiachilton
paper_title: LogoMotion: Visually Grounded Code Generation for Content-Aware
    Animation
paper_summary: 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_ai_summary: LogoMotion is an LLM-based system that generates animation code for logos, producing visually-grounded and content-aware animations.
paper_ai_keywords: LLM-based system visually-grounded program synthesis HTML representation primary and secondary elements animation code animation errors visually debug