Iterative Motion Editing with Natural Language
Purvi Goel Kuan-Chieh Wang C. Karen Liu Kayvon Fatahalian
Text-to-motion diffusion models can generate realistic animations from text
prompts, but do not support fine-grained motion editing controls. In this paper
we present a method for using natural language to iteratively specify local
edits to existing character animations, a task that is common in most computer
animation workflows. Our key idea is to represent a space of motion edits using
a set of kinematic motion operators that have well-defined semantics for how to
modify specific frames of a target motion. We provide an algorithm that
leverages pre-existing language models to translate textual descriptions of
motion edits to sequences of motion editing operators (MEOs). Given new
keyframes produced by the MEOs, we use diffusion-based keyframe interpolation
to generate final motions. Through a user study and quantitative evaluation, we
demonstrate that our system can perform motion edits that respect the
animator's editing intent, remain faithful to the original animation (they edit
the original animation, not dramatically change it), and yield realistic
character animation results.