Motion-Agent: A Conversational Framework for Human Motion Generation with LLMs

Qi Wu, Yubo Zhao, Yifan Wang, Xinhang Liu, Yu-Wing Tai, Chi-Keung Tang

Keywords: 3D human motion, multimodal LLM, motion generation, conversational AI

Abstract:

While previous approaches to 3D human motion generation have achieved notable success, they often rely on extensive training and are limited to specific tasks. To address these challenges, we introduce **Motion-Agent**, an efficient conversational framework designed for general human motion generation, editing, and understanding.
Motion-Agent employs an open-source pre-trained language model to develop a generative agent, **MotionLLM**, that bridges the gap between motion and text. This is accomplished by encoding and quantizing motions into discrete tokens that align with the language model's vocabulary. With only 1-3% of the model's parameters fine-tuned using adapters, MotionLLM delivers performance on par with diffusion models and other transformer-based methods trained from scratch. By integrating MotionLLM with GPT-4 without additional training, Motion-Agent is able to generate highly complex motion sequences through multi-turn conversations, a capability that previous models have struggled to achieve.
Motion-Agent supports a wide range of motion-language tasks, offering versatile capabilities for generating and customizing human motion through interactive conversational exchanges.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 3427