  Recent advancements in Multimodal Large Language Models (MM-LLMs) have
demonstrated promising potential in terms of generalization and robustness when
applied to different modalities. While previous works have already achieved 3D
human motion generation using various approaches including language modeling,
they mostly % are mostly carefully designed use specialized architecture and
are restricted to single-human motion generation. Inspired by the success of
MM-LLMs, we propose MotionLLM, a simple and general framework that can achieve
single-human, multi-human motion generation, and motion captioning by
fine-tuning pre-trained LLMs. Specifically, we encode and quantize motions into
discrete LLM-understandable tokens, which results in a unified vocabulary
consisting of both motion and text tokens. With only 1--3% parameters of the
LLMs trained by using adapters, our single-human motion generation achieves
comparable results to those diffusion models and other trained-from-scratch
transformer-based models. Additionally, we show that our approach is scalable
and flexible, allowing easy extension to multi-human motion generation through
autoregressive generation of single-human motions. Project page:
https://knoxzhao.github.io/MotionLLM
