LLAniMAtion：LLAMA驱动的手势动画
LLAniMAtion: LLAMA Driven Gesture Animation
AI总结：
针对角色动画中手势生成的需求，提出利用LLAMA2从文本提取的LLM特征生成手势的方法，实验证明其效果显著优于音频特征，且无需音频即可生成两类手势，为手势生成提供了新的特征来源。
摘要：
伴随语音的手势是对话中的重要模态，可提供语境与社交线索。在角色动画中，恰当且同步的手势能提升真实感，让交互智能体更具吸引力。以往自动生成手势的方法主要由音频驱动，利用音频信号中编码的韵律及语音相关内容。本文则尝试使用通过LLAMA2从文本中提取的LLM特征来生成手势。我们将其与音频特征进行对比，并在客观测试和用户研究中探索两种模态的结合。令人意外的是，结果显示仅使用LLAMA2特征的表现显著优于音频特征，且同时使用两种模态与单独使用LLAMA2特征相比无显著差异。我们证明了基于LLAMA2的模型无需任何音频输入即可生成节拍手势和语义手势，表明LLM可提供非常适合手势生成的丰富编码。
英文摘要：
Co-speech gesturing is an important modality in conversation, providing context and social cues. In character animation, appropriate and synchronised gestures add realism, and can make interactive agents more engaging. Historically, methods for automatically generating gestures were predominantly audio-driven, exploiting the prosodic and speech-related content that is encoded in the audio signal. In this paper we instead experiment with using LLM features for gesture generation that are extracted from text using LLAMA2. We compare against audio features, and explore combining the two modalities in both objective tests and a user study. Surprisingly, our results show that LLAMA2 features on their own perform significantly better than audio features and that including both modalities yields no significant difference to using LLAMA2 features in isolation. We demonstrate that the LLAMA2 based model can generate both beat and semantic gestures without any audio input, suggesting LLMs can provide rich encodings that are well suited for gesture generation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
