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LogoMotion: Visually-Grounded Code Synthesis for Creating and Editing Animation

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arxiv 2405.07065 v2 pith:IDBRMN5W submitted 2024-05-11 cs.HC

Vivian Liu , Rubaiat Habib Kazi , Li-Yi Wei , Matthew Fisher , Timothy Langlois , Seth Walker , Lydia Chilton This is my paper · ORCID
classification cs.HC
keywords animationcodelogomotionanimationscode-connectedcreatingeditediting
verification ladder T0 review T1 audit T2 compute T3 formal
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Creating animation takes time, effort, and technical expertise. To help novices with animation, we present LogoMotion, an AI code generation approach that helps users create semantically meaningful animation for logos. LogoMotion automatically generates animation code with a method called visually-grounded code synthesis and program repair. This method performs visual analysis, instantiates a design concept, and conducts visual checking to generate animation code. LogoMotion provides novices with code-connected AI editing widgets that help them edit the motion, grouping, and timing of their animation. In a comparison study on 276 animations, LogoMotion was found to produce more content-aware animation than an industry-leading tool. In a user evaluation (n=16) comparing against a prompt-only baseline, these code-connected widgets helped users edit animations with control, iteration, and creative expression.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Design Theater: Evaluating the Gap Between User-Facing Design Reasoning and Implementation in Generative UI Tools

    cs.AI 2026-07 conditional novelty 6.0 of 10

    Generative UI tools' stated design rationales are not fully implemented in about 25% of cases, with functional requirements failing most often.

  2. Multilingual Multimodal Software Developer for Code Generation

    cs.CL 2025-07 conditional novelty 6.0 of 10

    A 7B vision-language model trained on synthetic diagram-to-code data outperforms several larger open-weight models on a new 10-language UML/flowchart code-generation benchmark.

  3. AnyAni: An Interactive System with Generative AI for Animation Effect Creation and Code Understanding in Web Development

    cs.HC 2025-06 conditional novelty 6.0 of 10

    AnyAni combines LLM generation, a version tree, and video-based checking to help front-end developers create and understand web animations; a nine-person study reports usability gains over a chatbot baseline.

  4. MapStory: Prototyping Editable Map Animations with LLM Agents

    cs.HC 2025-05 conditional novelty 6.0 of 10

    Natural language scripts can be turned into editable, geospatially grounded map animations through MapStory's dual-agent LLM architecture.

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