Hierarchical Motion Captioning Utilizing External Text Data Source

• URL: http://arxiv.org/abs/2509.01471v1
• Date: Mon, 01 Sep 2025 13:39:14 GMT
• Title: Hierarchical Motion Captioning Utilizing External Text Data Source
• Authors: Clayton Leite, Yu Xiao,
• Abstract summary: This paper introduces a novel approach to enhance existing motion captioning methods.<n>We use large language models to create detailed descriptions corresponding to each high-level caption that appears in the motion-text datasets.<n>It aligns the detailed low-level captions with candidate high-level captions from additional text data sources, and combine them with motion features to fabricate precise high-level captions.
• Score: 1.2072875728151518
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces a novel approach to enhance existing motion captioning methods, which directly map representations of movement to high-level descriptive captions (e.g., ``a person doing jumping jacks"). The existing methods require motion data annotated with high-level descriptions (e.g., ``jumping jacks"). However, such data is rarely available in existing motion-text datasets, which additionally do not include low-level motion descriptions. To address this, we propose a two-step hierarchical approach. First, we employ large language models to create detailed descriptions corresponding to each high-level caption that appears in the motion-text datasets (e.g., ``jumping while synchronizing arm extensions with the opening and closing of legs" for ``jumping jacks"). These refined annotations are used to retrain motion-to-text models to produce captions with low-level details. Second, we introduce a pioneering retrieval-based mechanism. It aligns the detailed low-level captions with candidate high-level captions from additional text data sources, and combine them with motion features to fabricate precise high-level captions. Our methodology is distinctive in its ability to harness knowledge from external text sources to greatly increase motion captioning accuracy, especially for movements not covered in existing motion-text datasets. Experiments on three distinct motion-text datasets (HumanML3D, KIT, and BOTH57M) demonstrate that our method achieves an improvement in average performance (across BLEU-1, BLEU-4, CIDEr, and ROUGE-L) ranging from 6% to 50% compared to the state-of-the-art M2T-Interpretable.

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