Synthesis and Acquisition of Communicative Gestures

在具体的虚拟人类代理中，自然和上下文适当的手势的程序合成是具有挑战性的。Laban运动分析（LMA）提供了一个描述人类手势质量的系统，填补了预定义手势回放系统和人类动画师直觉之间的空白。构建了一个名为EMOTE的LMA计算模拟，其参数可以改变手臂手势运动的性能质量。EMOTE将以几种新的方式开发：*将EMOTE与代理模型连接，以便代理的情感，个性和交流需求为手势表现设置适当的EMOTE参数。*调查运动分析技术，从现场双或单摄像机视图提取EMOTE参数。*通过使用专业的LMA标记器来验证EMOTE参数的自动获取。*使用提取的参数来创建参数化动作的实例，这些实例可能随后用于动作、影响和方式描述，并最终用于现有电影或视频材料的内容导向分析。本研究将有助于将合成智能体动画技术建立在一个良好的经验基础上，提供证据表明计算机实际上可以观察到重要的运动品质，并导致内部智能体状态和外部行为品质之间的紧密联系。

Procedural synthesis of natural and contextually appropriate gestures in embodied virtual human agents is challenging. Laban Movement Analysis (LMA) offers a descriptive system for human gesture qualities that fills the gap between pre-defined gesture playback systems and human animator intuition. A computational analog of LMA called EMOTE has been constructed whose parameters modify the performance qualities of arm gesture movements. EMOTE will be developed in several new ways:* Connect EMOTE with an agent model so that an agent's affect, personality, and communicative needs set appropriate EMOTE parameters for gesture performance.* Investigate motion analysis techniques for extracting EMOTE parameters from live dual or single camera views.* Experimentally validate the automated acquisition of EMOTE parameters by using professional LMA notators for ground truth.* Use the extracted parameters to create instances of parameterized actions which may be subsequently used for action, affect, and manner descriptions and, ultimately, for content-directed analysis of existing film or video material.This study will help set synthetic agent animation techniques on a sound empirical footing, provide evidence that computers can in fact observe important motion qualities, and lead to strong connections between internal agent state and external behavior qualities.