LingoMotion: An Interpretable and Unambiguous Symbolic Representation for Human Motion

TL;DR

LingoMotion introduces an interpretable, hierarchical symbolic language for human motion, enabling clearer representation of simple and complex actions based on joint angles, morphology, and syntax.

Contribution

It proposes a novel hierarchical motion language inspired by natural language structure, enhancing interpretability and unambiguity over existing black-box methods.

Findings

High fidelity motion representation demonstrated on Motion-X dataset.

Effective encoding of simple actions like walking with attributes.

Potential for describing complex activities with sequences of motion symbols.

Abstract

Existing representations for human motion, such as MotionGPT, often operate as black-box latent vectors with limited interpretability and build on joint positions which can cause ambiguity. Inspired by the hierarchical structure of natural languages - from letters to words, phrases, and sentences - we propose LingoMotion, a motion language that facilitates interpretable and unambiguous symbolic representation for both simple and complex human motion. In this paper, we introduce the concept design of LingoMotion, including the definitions of motion alphabet based on joint angles, the morphology for forming words and phrases to describe simple actions like walking and their attributes like speed and scale, as well as the syntax for describing more complex human activities with sequences of words and phrases. The preliminary results, including the implementation and evaluation of motion alphabet using a large-scale motion dataset Motion-X, demonstrate the high fidelity of motion representation.