Bridging the Gap between Human Motion and Action Semantics via Kinematic Phrases

TL;DR

This paper introduces Kinematic Phrases (KP) to bridge the gap between human motion and action semantics, enabling more reliable motion understanding and generation through interpretable, abstracted kinematic descriptions.

Contribution

The paper proposes Kinematic Phrases (KP) as a novel abstraction to unify motion and semantics, improving reliability and interpretability in motion understanding and generation.

Findings

KP improves motion-semantic mapping accuracy.

KP enables automatic, bias-free motion-to-text conversion.

The approach outperforms previous methods in experiments.

Abstract

Motion understanding aims to establish a reliable mapping between motion and action semantics, while it is a challenging many-to-many problem. An abstract action semantic (i.e., walk forwards) could be conveyed by perceptually diverse motions (walking with arms up or swinging). In contrast, a motion could carry different semantics w.r.t. its context and intention. This makes an elegant mapping between them difficult. Previous attempts adopted direct-mapping paradigms with limited reliability. Also, current automatic metrics fail to provide reliable assessments of the consistency between motions and action semantics. We identify the source of these problems as the significant gap between the two modalities. To alleviate this gap, we propose Kinematic Phrases (KP) that take the objective kinematic facts of human motion with proper abstraction, interpretability, and generality. Based on KP, we can unify a motion knowledge base and build a motion understanding system. Meanwhile, KP can be automatically converted from motions to text descriptions with no subjective bias, inspiring Kinematic Prompt Generation (KPG) as a novel white-box motion generation benchmark. In extensive experiments, our approach shows superiority over other methods. Our project is available at https://foruck.github.io/KP/.