用大模型解析说话意图，生成语义连贯的手势。
SARGes: Semantically Aligned Reliable Gesture Generation via Intent Chain
- 通过意图链分解说话内容，生成结构化手势标签
- 手势标注准确率达50.2%，单次推理仅需0.4秒
- 可解释的推理路径，适合需要自然互动的应用
同步语音手势生成能提升人机交互的真实感，但生成具有语义意义的手势仍是难题。本文提出SARGes框架，利用大语言模型（LLMs）解析语音内容并生成可靠语义手势标签，进而指导有意义的同步语音手势合成。首先，构建了全面的同步语音手势图谱，并设计基于LLM的意图链推理机制，将手势语义按图谱标准系统分解为结构化推理步骤，有效引导模型生成上下文感知的手势标签。随后，构建了带有意图链标注的文本到手势标签数据集，并训练轻量级手势标签生成模型，从而指导生成可信且语义连贯的同步语音手势。实验表明，SARGes在手势标注上达到50.2%的准确率，支持高效单次推理（0.4秒）。该方法为语义手势合成提供了可解释的意图推理路径。
原文摘要 · Abstract (English)
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.
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