Synthesis and Acquisition of Communicative Gestures

在具体化的虚拟人代理中对自然且上下文适当的手势进行程序合成具有挑战性。 拉班运动分析（LMA）提供了一个描述系统的人类姿态的质量，填补了预定义的姿态播放系统和人类动画师的直觉之间的差距。 一个名为EMOTE的LMA的计算模拟已经构建，其参数修改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，以便代理的影响，个性和沟通需求为手势性能设置适当的EMOTE参数。研究运动分析技术，从实时双摄像机或单摄像机视图中提取EMOTE参数。*通过使用专业的LMA标注器进行地面实况的实验验证EMOTE参数的自动采集。*使用提取的参数来创建参数化动作的实例，这些参数化动作随后可用于动作、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。这项研究将有助于将合成代理动画技术建立在合理的经验基础上，提供计算机实际上可以观察重要运动质量的证据，并导致内部代理状态和外部行为品质之间的强联系。

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.