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

要旨

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.

著者
Vivian Liu
Columbia University, New York, New York, United States
Rubaiat Habib Kazi
Adobe Research, Seattle, Washington, United States
Li-Yi Wei
Adobe Research, San Jose, California, United States
Matthew David. Fisher
Adobe Systems, Palo Alto, California, United States
Timothy Richard. Langlois
Adobe Research, Seattle, Washington, United States
Seth Walker
Adobe Research, San Francisco, California, United States
Lydia B. Chilton
Columbia University, New York, New York, United States
DOI

10.1145/3706598.3714155

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714155

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Coding and Development

G418+G419
7 件の発表
2025-04-29 23:10:00
2025-04-30 00:40:00
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