Generative Motion Infilling From Imprecisely Timed Keyframes

Purvi Goel, Haotian Zhang, Karen Liu, Kayvon Fatahalian

Abstract:

Keyframes are a standard representation for kinematic motion specification. Recent learned motion-inbetweening methods use
keyframes as a way to control generative motion models, and are trained to generate life-like motion that matches the exact poses
and timings of input keyframes. However, the quality of generated motion may degrade if the timing of these constraints is not
perfectly consistent with the desired motion. Unfortunately, correctly specifying keyframe timings is a tedious and challenging
task in practice. Our goal is to create a system that synthesizes high-quality motion from keyframes, even if keyframes are
imprecisely timed. We present a method that allows constraints to be retimed as part of the generation process. Specifically,
we introduce a novel model architecture that explicitly outputs a time-warping function to correct mistimed keyframes, and
spatial residuals that add pose details. We demonstrate how our method can automatically turn approximately timed keyframe
constraints into diverse, realistic motions with plausible timing and detailed submovements.