Motion Flow Matching for Human Motion Synthesis and Editing
This addresses the need for efficient and effective human motion generation and editing in computer animation, representing a strong specific gain rather than a broad paradigm shift.
The paper tackles the problem of slow sampling and error accumulation in human motion synthesis by proposing Motion Flow Matching, which reduces sampling steps from thousands to ten while achieving comparable performance on benchmarks and setting a new state-of-the-art Fréchet Inception Distance on the KIT-ML dataset.
Human motion synthesis is a fundamental task in computer animation. Recent methods based on diffusion models or GPT structure demonstrate commendable performance but exhibit drawbacks in terms of slow sampling speeds and error accumulation. In this paper, we propose \emph{Motion Flow Matching}, a novel generative model designed for human motion generation featuring efficient sampling and effectiveness in motion editing applications. Our method reduces the sampling complexity from thousand steps in previous diffusion models to just ten steps, while achieving comparable performance in text-to-motion and action-to-motion generation benchmarks. Noticeably, our approach establishes a new state-of-the-art Fréchet Inception Distance on the KIT-ML dataset. What is more, we tailor a straightforward motion editing paradigm named \emph{sampling trajectory rewriting} leveraging the ODE-style generative models and apply it to various editing scenarios including motion prediction, motion in-between prediction, motion interpolation, and upper-body editing. Our code will be released.