MotionGPT: Human Motion as a Foreign Language

TL;DR

MotionGPT introduces a novel approach to treat human motion as a language, enabling a unified model that excels in various motion-related tasks through motion-language pre-training and prompt-based fine-tuning.

Contribution

It is the first to unify human motion modeling with language models using motion tokens and pre-training, enhancing performance across multiple motion tasks.

Findings

Achieves state-of-the-art results in text-driven motion generation.

Excels in motion captioning and prediction tasks.

Demonstrates versatility of motion as a language for modeling.

Abstract

Though the advancement of pre-trained large language models unfolds, the exploration of building a unified model for language and other multi-modal data, such as motion, remains challenging and untouched so far. Fortunately, human motion displays a semantic coupling akin to human language, often perceived as a form of body language. By fusing language data with large-scale motion models, motion-language pre-training that can enhance the performance of motion-related tasks becomes feasible. Driven by this insight, we propose MotionGPT, a unified, versatile, and user-friendly motion-language model to handle multiple motion-relevant tasks. Specifically, we employ the discrete vector quantization for human motion and transfer 3D motion into motion tokens, similar to the generation process of word tokens. Building upon this "motion vocabulary", we perform language modeling on both motion and text in a unified manner, treating human motion as a specific language. Moreover, inspired by prompt learning, we pre-train MotionGPT with a mixture of motion-language data and fine-tune it on prompt-based question-and-answer tasks. Extensive experiments demonstrate that MotionGPT achieves state-of-the-art performances on multiple motion tasks including text-driven motion generation, motion captioning, motion prediction, and motion in-between.