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

职业：艺术和电子交互新时代的角色动画和手势生成模型

• 批准号: 0845529
• 负责人: Michael Neff
• 金额: $ 58.13万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0845529&HistoricalAwards=false
• 关键词: CAREER; Generative; Models; Character; Animation

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.

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。本研究的动机问题是确定如何建立用于角色动画应用程序中的表现性人体运动的计算模型。 这个问题的满意解决方案必须允许高度的控制，以便可以为任何上下文定制角色移动。 这项工作将统一传统上独立的知识驱动和数据驱动的方法来角色动画，建立在知识驱动技术和运动捕捉数据的现实主义的内在控制。 将采用基于特征的方法开发生成模型。 在这种方法中，将与运动专业人员协商确定关键特征集，并且在运动捕捉工作室中工作的专业表演者将提供对这些特征的范围进行采样的数据。 将使用程序和学习技术相结合，从这些数据中开发每个功能的计算模型。 最终目标是风格定义，其中运动风格的明确方面可以通过计算表示。 这将通过一个软件框架支持运动分析和运动生成，该框架允许将这些功能中的每一个组合和表达。 主要应用包括会话代理模型和一系列动画工具。这项工作通过开发新的表达运动的计算模型和提供对人类运动本质的更深入了解而造福社会。 运动的计算模型提供有意义的，细粒度的控制是必不可少的一系列应用程序，包括虚拟世界，如第二人生，会话代理，远程协作系统，培训环境，游戏和其他互动，基于角色的媒体。 这些模型将通过结合计算机动画研究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。 该研究将整合计算机科学家，数字艺术家和运动专业人士，为技术开发带来广泛的见解，并在这些通常不同的群体之间提供交叉施肥。 研究结果将广泛发表，并导致新的计算工具，可用于一系列应用。