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arXiv · 2411.16805

Human Motion Instruction Tuning

Lei Li·Sen Jia·Jianhao Wang·Zhongyu Jiang·Feng Zhou·Ju Dai·Tianfang Zhang·Zongkai Wu·Jenq-Neng Hwang
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Abstract

This paper presents LLaMo (Large Language and Human Motion Assistant), a multimodal framework for human motion instruction tuning. In contrast to conventional instruction-tuning approaches that convert non-linguistic inputs, such as video or motion sequences, into language tokens, LLaMo retains motion in its native form for instruction tuning. This method preserves motion-specific details that are often diminished in tokenization, thereby improving the model's ability to interpret complex human behaviors. By processing both video and motion data alongside textual inputs, LLaMo enables a flexible, human-centric analysis. Experimental evaluations across high-complexity domains, including human behaviors and professional activities, indicate that LLaMo effectively captures domain-specific knowledge, enhancing comprehension and prediction in motion-intensive scenarios. We hope LLaMo offers a foundation for future multimodal AI systems with broad applications, from sports analytics to behavioral prediction. Our code and models are available on the project website: https://github.com/ILGLJ/LLaMo.

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Lei Li, Sen Jia, Jianhao Wang, Zhongyu Jiang, Feng Zhou, Ju Dai, Tianfang Zhang, Zongkai Wu, Jenq-Neng Hwang. 2024-11-25. Human Motion Instruction Tuning. https://arxiv.org/abs/2411.16805

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