Keyframer: Empowering Animation Design using Large Language Models
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Abstract (arXiv)
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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Feb 2024.
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Citing papers per year: 2024: 4; 2025: 4; 2026: 1.
9 papers.
- 2024Chat2SVG: Vector Graphics Generation with Large Language Models and Image Diffusion Modelscited by 36
- 2024Not Just Novelty: A Longitudinal Study on Utility and Customization of an AI Workflowcited by 13
- 2025MoVer: Motion Verification for Motion Graphics Animationscited by 10
- 2024DreamStruct: Understanding Slides and User Interfaces via Synthetic Data Generationcited by 8
- 2024Substance Beats Style: Why Beginning Students Fail to Code with LLMscited by 5
- 2025GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Designcited by 5
- 2026LottieGPT: Tokenizing Vector Animation for Autoregressive Generationcited by 5
- 2025DashChat: Interactive Authoring of Performance Dashboard Design Prototypes through Conversation with LLM-Powered Agentcited by 1
- 2025MG-Gen: Single Image to Motion Graphics Generationcited by 0
- 2026LottieGPT: Tokenizing Vector Animation for Autoregressive Generation13 Apr 2026
- 2025GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Design21 Aug 2025
- 2025DashChat: Interactive Authoring of Performance Dashboard Design Prototypes through Conversation with LLM-Powered Agent17 Apr 2025
- 2025MG-Gen: Single Image to Motion Graphics Generation3 Apr 2025
- 2025MoVer: Motion Verification for Motion Graphics Animations19 Feb 2025
- 2024Chat2SVG: Vector Graphics Generation with Large Language Models and Image Diffusion Models25 Nov 2024
- 2024Substance Beats Style: Why Beginning Students Fail to Code with LLMs15 Oct 2024
- 2024DreamStruct: Understanding Slides and User Interfaces via Synthetic Data Generation30 Sep 2024
- 2024Not Just Novelty: A Longitudinal Study on Utility and Customization of an AI Workflow15 Feb 2024