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Keyframer: Empowering Animation Design using Large Language Models

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arxiv 2402.06071 v2 pith:WTZHKBFD submitted 2024-02-08 cs.HC

Tiffany Tseng , Ruijia Cheng , Jeffrey Nichols This is my paper · ORCID
classification cs.HC
keywords designanimationanimationslanguageiterativekeyframernaturalresponse
verification ladder T0 review T1 audit T2 compute T3 formal
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Creating 2D animations is a complex, iterative process requiring continuous adjustments to movement, timing, and coordination of multiple elements within a scene. To support designers of varying levels of experience with animation design and implementation, we developed Keyframer, a design tool that generates animation code in response to natural language prompts, enabling users to preview rendered animations inline and edit them directly through provided editors. Through a user study with 13 novices and experts in animation design and programming, we contribute 1) a categorization of semantic prompt types for describing motion and identification of a 'decomposed' prompting style where users continually adapt their goals in response to generated output; and 2) design insights on supporting iterative refinement of animations through the combination of direct editing and natural language interfaces.

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Cited by 6 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. fog: Expressing Motion and Emotion through Function Composition of AI-Generated Code

    cs.HC 2026-07 conditional novelty 6.0 of 10

    fog composes LLM-generated motion functions (verbs/adverbs/gestures/emotions) so people recognize intended semantics in Heider-Simmel animations at 68% accuracy and iterate faster than pure prompting.

  3. Flowcode: An AI-Powered Programming Environment for Scaffolding Iteration in Creative Computing Education

    cs.HC 2026-07 conditional novelty 6.0 of 10

    An iterative design of Flowcode shows that LLM-generated flowcharts plus step-by-step fill-in-the-blank explanations help novices orient, navigate, and extend found creative-coding projects.

  4. KinemaFX: A Kinematic-Driven Interactive System for Particle Effect Exploration and Customization

    cs.HC 2025-07 conditional novelty 6.0 of 10

    A kinematic-aware search and exploration system helps non-experts create customized particle effect artworks through text, simple shapes, motion paths, and implicit preference-guided iteration.

  5. 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.

  6. GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Design

    cs.HC 2025-08 conditional novelty 5.0 of 10

    GenTune improves AI image refinement by tracing image regions back to prompt labels and allowing element-level, semantic-guided edits.

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