M$^3$GPT: An Advanced Multimodal, Multitask Framework for Motion Comprehension and Generation

• URL: http://arxiv.org/abs/2405.16273v5
• Date: Sat, 02 Nov 2024 04:39:28 GMT
• Title: M$^3$GPT: An Advanced Multimodal, Multitask Framework for Motion Comprehension and Generation
• Authors: Mingshuang Luo, Ruibing Hou, Zhuo Li, Hong Chang, Zimo Liu, Yaowei Wang, Shiguang Shan,
• Abstract summary: M$3$GPT is an advanced $textbfM$ultimodal, $textbfM$ultitask framework for comprehension and generation. We employ discrete vector quantization for multimodal conditional signals, such as text, music and motion/dance, enabling seamless integration into a large language model. M$3$GPT learns to model the connections and synergies among various motion-relevant tasks.
• Score: 78.77004913030285
• License:
• Abstract: 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 conditional 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 motion generation directly in the raw motion space. This strategy circumvents the information loss associated with a discrete tokenizer, resulting in more detailed and comprehensive motion 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. Project page: \url{https://github.com/luomingshuang/M3GPT}.

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