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.

本文提出了一种统一的，多功能的运动语言模型 MotionGPT，旨在通过将语言数据与大规模运动模型相结合，开发出能够增强涉及运动的任务的预训练模型，实现对文本驱动运动生成、运动字幕制作、运动预测和中间运动状态生成等多项任务的最先进表现。