MotionChain: Conversational Motion Controllers via Multimodal Prompts
<|reference_start|>MotionChain: Conversational Motion Controllers via Multimodal Prompts: Recent advancements in language models have demonstrated their adeptness in conducting multi-turn dialogues and retaining conversational context. However, this proficiency remains largely unexplored in other multimodal generative models, particularly in human motion models. By integrating multi-turn conversations in controlling continuous virtual human movements, generative human motion models can achieve an intuitive and step-by-step process of human task execution for humanoid robotics, game agents, or other embodied systems. In this work, we present MotionChain, a conversational human motion controller to generate continuous and long-term human motion through multimodal prompts. Specifically, MotionChain consists of multi-modal tokenizers that transform various data types such as text, image, and motion, into discrete tokens, coupled with a Vision-Motion-aware Language model. By leveraging large-scale language, vision-language, and vision-motion data to assist motion-related generation tasks, MotionChain thus comprehends each instruction in multi-turn conversation and generates human motions followed by these prompts. Extensive experiments validate the efficacy of MotionChain, demonstrating state-of-the-art performance in conversational motion generation, as well as more intuitive manners of controlling and interacting with virtual humans.<|reference_end|>
@article{jiang2024motionchain:,
    title={MotionChain: Conversational Motion Controllers via Multimodal Prompts},
    author={Biao Jiang, Xin Chen, Chi Zhang, Fukun Yin, Zhuoyuan Li, Gang YU,
  Jiayuan Fan},
    journal={arXiv preprint arXiv:2404.01700},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01700},
    primaryClass={cs.CV}
}