Notational Animating: An Interactive Approach to Creating and Editing Animation Keyframes

2026-03-06

AI Summary

This work proposes “annotation-driven animation,” a novel interaction paradigm that formalizes hand-drawn annotations—commonly used yet ambiguous, context-dependent, and unstructured in traditional animation—into a structured source-path-target representation. By integrating generative AI, the system automatically synthesizes keyframes from these annotations and employs dynamic UI controls to enable fine-grained user adjustment and disambiguation. The approach establishes a closed-loop human-AI collaboration framework that bridges imprecise sketches to executable animations. Preliminary user studies demonstrate its effectiveness and practicality in supporting efficient, intuitive keyframe generation and editing.

Technology Category

• Humans and AI: Interaction Techniques and Devices
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Computer Vision: Computational Photography, Image & Video Synthesis

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

We introduce the concept of notational animating, an interaction paradigm for animation authoring where users sketch high-level notations over static drawings to indicate intended motions, which are then interpreted by automatic methods (e.g., GenAI models) to generate animation keyframes. Sketched notations have long served as cognitive instruments for animators, capturing forces, poses, dynamics, paths, and other animation features. However, such notations are often context-dependent, non-categorical, ambiguous, and composable based on our analysis of real-world animator-produced sketches. To facilitate interpretation, we first formalize these notations into a structured animation representation (i.e., source, path, and target). We then built an animation authoring system that translates high-level notations into the formalized intended animation, provides dynamic UI widgets for fine-grained parameter control, and establishes a closed feedback loop to resolve ambiguity. Finally, through a preliminary study with animators, we assess the usability of notational animating, reflect its affordance, and identify its contexts of use.

Problem

• notational animating
• animation keyframes
• sketch notations
• animation authoring
• ambiguous notation

Innovation

• notational animating
• animation keyframes
• sketch-based interaction
• generative AI
• structured animation representation