SARGes: Semantically Aligned Reliable Gesture Generation via Intent Chain
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915213763149824 |
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| author | Gao, Nan Bao, Yihua Weng, Dongdong Zhao, Jiayi Li, Jia Zhou, Yan Wan, Pengfei Zhang, Di |
| author_facet | Gao, Nan Bao, Yihua Weng, Dongdong Zhao, Jiayi Li, Jia Zhou, Yan Wan, Pengfei Zhang, Di |
| contents | Co-speech gesture generation enhances human-computer interaction realism through speech-synchronized gesture synthesis. However, generating semantically meaningful gestures remains a challenging problem. We propose SARGes, a novel framework that leverages large language models (LLMs) to parse speech content and generate reliable semantic gesture labels, which subsequently guide the synthesis of meaningful co-speech gestures.First, we constructed a comprehensive co-speech gesture ethogram and developed an LLM-based intent chain reasoning mechanism that systematically parses and decomposes gesture semantics into structured inference steps following ethogram criteria, effectively guiding LLMs to generate context-aware gesture labels. Subsequently, we constructed an intent chain-annotated text-to-gesture label dataset and trained a lightweight gesture label generation model, which then guides the generation of credible and semantically coherent co-speech gestures. Experimental results demonstrate that SARGes achieves highly semantically-aligned gesture labeling (50.2% accuracy) with efficient single-pass inference (0.4 seconds). The proposed method provides an interpretable intent reasoning pathway for semantic gesture synthesis. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_20202 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SARGes: Semantically Aligned Reliable Gesture Generation via Intent Chain Gao, Nan Bao, Yihua Weng, Dongdong Zhao, Jiayi Li, Jia Zhou, Yan Wan, Pengfei Zhang, Di Computation and Language Artificial Intelligence Human-Computer Interaction Robotics Co-speech gesture generation enhances human-computer interaction realism through speech-synchronized gesture synthesis. However, generating semantically meaningful gestures remains a challenging problem. We propose SARGes, a novel framework that leverages large language models (LLMs) to parse speech content and generate reliable semantic gesture labels, which subsequently guide the synthesis of meaningful co-speech gestures.First, we constructed a comprehensive co-speech gesture ethogram and developed an LLM-based intent chain reasoning mechanism that systematically parses and decomposes gesture semantics into structured inference steps following ethogram criteria, effectively guiding LLMs to generate context-aware gesture labels. Subsequently, we constructed an intent chain-annotated text-to-gesture label dataset and trained a lightweight gesture label generation model, which then guides the generation of credible and semantically coherent co-speech gestures. Experimental results demonstrate that SARGes achieves highly semantically-aligned gesture labeling (50.2% accuracy) with efficient single-pass inference (0.4 seconds). The proposed method provides an interpretable intent reasoning pathway for semantic gesture synthesis. |
| title | SARGes: Semantically Aligned Reliable Gesture Generation via Intent Chain |
| topic | Computation and Language Artificial Intelligence Human-Computer Interaction Robotics |
| url | https://arxiv.org/abs/2503.20202 |