Summary of Human Motion Instruction Tuning, by Lei Li and Sen Jia and Wang Jianhao and Zhongyu Jiang and Feng Zhou and Ju Dai and Tianfang Zhang and Wu Zongkai and Jenq-neng Hwang
Human Motion Instruction Tuning
by Lei Li, Sen Jia, Wang Jianhao, Zhongyu Jiang, Feng Zhou, Ju Dai, Tianfang Zhang, Wu Zongkai, Jenq-Neng Hwang
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents LLaMo, a multimodal framework for human motion instruction tuning. Unlike traditional approaches that convert non-linguistic inputs into language tokens, LLaMo retains motion in its native form. This allows the model to better interpret complex human behaviors by preserving motion-specific details lost during tokenization. The framework processes video, motion data, and textual inputs simultaneously, enabling a flexible analysis of human-centric phenomena. Experimental results across high-complexity domains demonstrate that LLaMo effectively captures domain-specific knowledge, enhancing comprehension and prediction in motion-intensive scenarios. This work offers a foundation for future multimodal AI systems with broad applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces LLaMo, a new way to teach machines about human movement. Instead of changing the movement into words, LLaMo keeps it as is. This helps the machine understand complex movements better. The approach combines video and movement data with text to analyze human actions. Tests show that LLaMo works well in different areas, like sports analytics or predicting behavior. |
Keywords
» Artificial intelligence » Instruction tuning » Tokenization
