CAREER: Generative Models for Character Animation and Gesture in the New Age of Art and Electronic Interaction

该奖项是根据2009年《美国复苏和再投资法案》(公法111-5)提供资金的。这项研究的动机问题是确定如何建立用于角色动画应用的富有表现力的人体运动的计算模型。对此问题的满意解决方案必须允许高度控制，以便可以针对任何上下文定制角色移动。这项工作将统一传统上分离的知识驱动和数据驱动的角色动画方法，建立在知识驱动技术固有的控制和运动捕捉数据的现实主义基础上。将使用基于特征的方法来开发生成性模型。在这种方法中，关键功能集将在咨询动作专业人员后确定，在动作捕捉工作室工作的专业表演者将提供这些功能范围内的数据采样。每个特征的计算模型将使用程序和学习技术的组合从这些数据中开发出来。最终目标是风格定义，在风格定义中，动作风格的显性方面可以通过计算来表示。这将通过一个软件框架支持移动分析和移动生成，该软件框架允许组合和表达这些特征中的每一个。主要应用包括对话代理的模型和一系列动画工具。这项工作通过开发新的可表达运动的计算模型并通过提供对人类运动本质的更深层次的洞察而造福于社会。提供有意义的细粒度控制的运动计算模型对于一系列应用至关重要，包括虚拟世界，如Second Life、对话代理、远程协作系统、培训环境、游戏和其他基于角色的互动媒体。这些模型将结合计算机动画研究中的两个主要趋势来开发，一个是基于现有知识的显式表示来构建模型，另一个是挖掘运动数据来创建模型。这项研究将整合计算机科学家、数字艺术家和运动专业人士，为技术发展带来一系列广泛的见解，并在这些通常不同的群体之间提供交叉培养。研究结果将被广泛发表，并导致可在一系列应用中使用的新计算工具。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). The motivating problem of this research is to determine how to build computational models of expressive human movement for use in character animation applications. Satisfactory solutions to this problem must allow a high degree of control so that character movement can be customized for any context. This work will unify traditionally separate knowledge-driven and data-driven approaches to character animation, building on the control inherent in knowledge-driven techniques and the realism of motion capture data. A feature-based approach will be used to develop generative models. In this approach, a key-feature set will be determined in consultation with movement professionals, and professional performers working in a motion capture studio will provide data sampling the range of these features. Computational models of each feature will be developed from this data using a combination of procedural and learning techniques. The end goal is style-definition, in which explicit aspects of movement style can be represented computationally. This will support both movement analysis and movement generation through a software framework that allows each of these features to be combined and expressed. Key applications include models for conversational agents and a range of animation tools.This work benefits society through the development of new computational models of expressive movement and by providing deeper insights into the nature of human motion. Computational models of movement that offer meaningful, fine-grained control are essential for a range of applications, including virtual worlds like Second Life, conversational agents, remote collaboration systems, training environments, games and other interactive, character based media. These models will be developed by combining two main trends in computer animation research, one that builds models based on explicit representations of existing knowledge and one that mines movement data to create models. The research will integrate computer scientists, digital artists and movement professionals, bringing a broad set of insights to technology development and providing cross-fertilization between these normally disparate groups. Research results will be published broadly and lead to new computational tools that can be used in a range of applications.