M$^3$GPT: An Advanced Multimodal, Multitask Framework for Motion Comprehension and Generation
<|reference_start|>M$^3$GPT: An Advanced Multimodal, Multitask Framework for Motion Comprehension and Generation: This paper presents M$^3$GPT, an advanced $\textbf{M}$ultimodal, $\textbf{M}$ultitask framework for $\textbf{M}$otion comprehension and generation. M$^3$GPT operates on three fundamental principles. The first focuses on creating a unified representation space for various motion-relevant modalities. We employ discrete vector quantization for multimodal control and generation signals, such as text, music and motion/dance, enabling seamless integration into a large language model (LLM) with a single vocabulary. The second involves modeling model generation directly in the raw motion space. This strategy circumvents the information loss associated with discrete tokenizer, resulting in more detailed and comprehensive model generation. Third, M$^3$GPT learns to model the connections and synergies among various motion-relevant tasks. Text, the most familiar and well-understood modality for LLMs, is utilized as a bridge to establish connections between different motion tasks, facilitating mutual reinforcement. To our knowledge, M$^3$GPT is the first model capable of comprehending and generating motions based on multiple signals. Extensive experiments highlight M$^3$GPT's superior performance across various motion-relevant tasks and its powerful zero-shot generalization capabilities for extremely challenging tasks.<|reference_end|>
@article{luo2024m$^3$gpt:,
    title={M$^3$GPT: An Advanced Multimodal, Multitask Framework for Motion
  Comprehension and Generation},
    author={Mingshuang Luo, Ruibing Hou, Zhuo Li, Hong Chang, Zimo Liu, Yaowei
  Wang and Shiguang Shan},
    journal={arXiv preprint arXiv:2405.16273},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16273},
    primaryClass={cs.CV}
}