Keyframer: Empowering Animation Design using Large Language Models

Tiffany Tseng; Ruijia Cheng; Jeffrey Nichols

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arXiv · 2402.06071

Keyframer: Empowering Animation Design using Large Language Models

Tiffany Tseng·Ruijia Cheng·Jeffrey Nichols

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Abstract

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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Tiffany Tseng, Ruijia Cheng, Jeffrey Nichols. 2024-02-08. Keyframer: Empowering Animation Design using Large Language Models. https://arxiv.org/abs/2402.06071

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