HCC-GV: Small: Generating Animal Avatar Animation with Specific Identifiable Traits Based Upon Viewer Perception of Real Animals

HCC-GV：小型：根据观众对真实动物的感知生成具有特定可识别特征的动物头像动画

• 批准号: 1016795
• 负责人: Timothy McLaughlin
• 金额: $ 50万
• 依托单位: Texas A&M Research Foundation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1016795&HistoricalAwards=false
• 关键词: HCC; GV; Small; Generating; Animal

Computing devices have transformed person-to-person communication, dramatically altering how we present ourselves to the world. As the use of computer character animation rapidly expands into 3D digital media in mobile devices, social networking, and massively multiplayer online games, the demand for distinctive personalized digital avatars is beyond the capacity of key-frame animation, beyond the range of motion capture, and beyond the expressive capability of rule-based animation. The PI's goal in this project is to define a new framework that transforms biological locomotion into a content form, classified by identity-rich features which can be synthesized for use in animating animals, whether real or imagined. The research focuses on how species, age, and weight are perceived by people viewing real animals, with the intention of making virtual animals, or humans representing themselves as animal avatars, more expressive. Project outcomes will include a technique for procedural generation of animation for novel digital creatures that is capable of movement patterns signifying a specific animal species, age as young or old, and weight as heavy or light. The PI argues that to develop this new way of creating and managing expressive animation, we need a better understanding of how we perceive motion itself. To this end, the PI and his team will borrow from biological motion studies to determine what level of detail of motion information is required for recognition. He will use eye tracking to determine where the information is found in animal motion, and will employ linear analysis to decompose the motion and re-inject the identity-laden elements into novel animal forms. Thus, the work will combine three areas of research - perception of biological motion, eye tracking, and synthesis of gait patterns - to develop the foundation for a generative system for animal avatar animation. Broader Impacts: This project combines the use of computational systems to both analyze how we perceive motion and to synthesize new, novel motion. The research will contribute to our understanding of human perception of biological motion, and expand the performance range and emotional impact of synthesized animation. Techniques for isolating the spatio-temporal information that leads to recognition of identifiable traits will expand beyond the perception of humans and into the domain of animal motion, thereby opening the door to creating animal avatars that are expressive through motion in a variety of ways that communicate personality. Project outcomes will thus be of interest to creators of digital content for games, immersive virtual worlds, and cinema. The PI expects this work will lead to expansion of both the range of traits that can be procedurally applied to animation and the sophistication of novel gait generation methods.

计算设备已经改变了人与人之间的交流，极大地改变了我们向世界展示自己的方式。 随着计算机角色动画的使用迅速扩展到移动的设备、社交网络和大型多人在线游戏中的3D数字媒体，对独特的个性化数字化身的需求超出了关键帧动画的能力、超出了动作捕捉的范围、并且超出了基于规则的动画的表达能力。 PI在这个项目中的目标是定义一个新的框架，将生物运动转化为内容形式，通过身份丰富的功能进行分类，这些功能可以合成用于动画动物，无论是真实的还是想象的。 这项研究的重点是人们如何看待真实的动物的物种，年龄和体重，目的是使虚拟动物或人类代表自己作为动物化身，更具表现力。 项目成果将包括一种新的数字生物的动画程序生成技术，该技术能够表示特定动物物种的运动模式，年龄是年轻还是年老，重量是重还是轻。 PI认为，要开发这种创建和管理表现性动画的新方法，我们需要更好地理解我们如何感知运动本身。 为此，PI和他的团队将借鉴生物运动研究，以确定识别所需的运动信息的详细程度。 他将使用眼动追踪来确定在动物运动中发现的信息，并将采用线性分析来分解运动，并将身份负载元素重新注入到新的动物形式中。 因此，这项工作将结合联合收割机三个领域的研究-感知生物运动，眼睛跟踪和步态模式的合成-发展的基础，为动物化身动画生成系统。 更广泛的影响：该项目结合了计算系统的使用，既分析我们如何感知运动，又合成新的，新颖的运动。 该研究将有助于我们理解人类对生物运动的感知，并扩大合成动画的表现范围和情感影响。 用于隔离时空信息的技术将扩展到人类的感知之外，并进入动物运动的领域，从而打开了创造动物化身的大门，这些动物化身通过各种方式表达个性。 因此，项目成果将引起游戏、沉浸式虚拟世界和电影数字内容创作者的兴趣。 PI预计这项工作将导致扩展可以在程序上应用于动画的特征范围和新颖步态生成方法的复杂性。