MotionGPT: Finetuned LLMs are General-Purpose Motion Generators
Authors: name: Yaqi Zhang, name: Di Huang, name: Bin Liu, name: Shixiang Tang, name: Yan Lu, name: Lu Chen, name: Lei Bai, name: Qi Chu, name: Nenghai Yu, name: Wanli Ouyang

Generating realistic human motion from given action descriptions has
experienced significant advancements because of the emerging requirement of
digital humans. While recent works have achieved impressive results in
generating motion directly from textual action descriptions, they often support
only a single modality of the control signal, which limits their application in
the real digital human industry. This paper presents a Motion General-Purpose
generaTor (MotionGPT) that can use multimodal control signals, e.g., text and
single-frame poses, for generating consecutive human motions by treating
multimodal signals as special input tokens in large language models (LLMs).
Specifically, we first quantize multimodal control signals into discrete codes
and then formulate them in a unified prompt instruction to ask the LLMs to
generate the motion answer. Our MotionGPT demonstrates a unified human motion
generation model with multimodal control signals by tuning a mere 0.4% of LLM
parameters. To the best of our knowledge, MotionGPT is the first method to
generate human motion by multimodal control signals, which we hope can shed
light on this new direction. Codes shall be released upon acceptance.