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

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在移动设备、社交网络和大型多人在线游戏中的使用迅速扩展到3D数字媒体，对独特的个性化数字角色的需求超出了关键帧动画的能力，超出了动作捕捉的范围，也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，根据身份丰富的特征进行分类，这些特征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在观看真实动物时对物种、年龄和体重的感知，目的是让虚拟动物或人类以动物形象代表自己，更具表现力。项目成果将包括一种程序生成动画的新数字生物技术，能够表示特定动物物种的运动模式，年龄是年轻的还是年老的，重量是重的还是轻的。PI认为，要开发这种创造和管理富有表现力的动画的新方法，我们需要更好地了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来确定识别所需的运动信息细节水平。他将使用眼动追踪来确定动物运动中的信息，并将使用线性分析来分解运动，并将身份负载元素重新注入新的动物形态中。因此，这项工作将结合三个研究领域——生物运动感知、眼动追踪和步态模式合成——为动物化身动画的生成系统奠定基础。更广泛的影响：该项目结合了计算系统的使用来分析我们如何感知运动并合成新的、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，扩大合成动画的表现范围和情感影响。分离时空信息的技术可以识别可识别的特征，这种技术将超越人类的感知，扩展到动物运动领域，从而为创造通过运动表达个性的各种方式的动物化身打开大门。因此，项目成果将引起游戏、沉浸式虚拟世界和电影的数字内容创作者的兴趣。PI期望这项工作将导致扩展可应用于动画的程序特征范围和新步态生成方法的复杂性。

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.