CAREER: Reusable, Realistic Motion Libraries for Computer Animation

职业:可重复使用的、逼真的计算机动画运动库

基本信息

  • 批准号:
    0092970
  • 负责人:
  • 金额:
    $ 32.5万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2001
  • 资助国家:
    美国
  • 起止时间:
    2001-07-01 至 2007-06-30
  • 项目状态:
    已结题

项目摘要

Animation is a tremendously powerful medium of expression.Ideas that would be complex or even impossible to expresswith still images or words can easily be conveyed usinganimated sequences. Unfortunately, even when the concept tobe conveyed via animation is simple, the process ofproducing the animation is inordinately difficult. Indeed,this is one of the reasons computer animation has become sodominant - it holds the promise of automating, and thussignificantly simplifying, the production process. Sadly,the process of transferring ideas into computer animationsis still far from simple. For example, the animated featurefilm Toy Story 2 required extremely hard work by as many as200 highly skilled computer and animation experts for morethan three years.My career goal is to develop the concepts and algorithmsthat will enable effortless, rapid development of realisticanimations that effectively convey the creator's purpose.This research hopes to create the theoretical andexperimental foundation for building much more powerfulanimation tools than exist today. Such tools will sharplyreduce the production time needed by skilled animators and,more importantly, enable average computer users to expresstheir ideas through animation. Such tools will also helpeducators teach more effectively and allow each one of us tobecome creators and directors of personal and fictionalstories. Only when animation production is easier willanimation be as commonplace on the web as images and textare today. Moreover, intuitive methods for creatingrealistic human motion will further enable the creation ofhuman avatars in tele-presence applications, newanthropomorphic human-computer interfaces, and realisticdigital actors in feature films and video games. Computeranimation will not achieve its full potential until 1)animation tools require little or no skill, 2) an animator'screativity is not stifled by the limitations of theanimation tools, and 3) the animation systems are availableto every person with a computer and a story to tell. I hopeto significantly contribute towards these goals.At the heart of this research is the observation that theanimator's creativity will not be fully realized until itbecomes easier to produce realistic motion sequences. Thisproblem is tremendously challenging because the underlyingphysical models of motion are difficult to create andcontrol. I believe that the solution lies in the creation ofhighly-flexible realistic motion libraries, together withtools to modify them intuitively and extensively. The powerof libraries stems from the ability to reuse alreadyexisting high-quality motion, reducing the need foranimation skills. In this paradigm, the process of animationturns into selecting the specific motion library, andmodifying a set of motion properties that transform theoriginal motion into a final animation.In my research, I propose to develop a methodology forcreating and using realistic motion libraries flexibleenough to be used in a wide range of applications. In 1995,I proposed the use of motion transformation as new way tocreate animations - a method fundamentally different fromthe traditional way of creating animations from scratch.This transformation approach is particularly useful formodifying realistic motion data captured from real-worldactors. Unfortunately, during the transformation process,much of the realism tends to be lost. Recently, I publisheda novel transformation approach which demonstrates thatanimations can be intuitively transformed into a wide rangeof new sequences without violating the fundamental dynamicproperties of motion. The realism is preserved bymaintaining a model of the dynamic and biomechanicproperties of the animated character.In the future, I will further develop mathematical modelsthat can be combined with real-world data to create reusablemotion libraries. Aside from preserving the realism ofmotion, the most important requirement for effective motionlibraries is the flexibility with which the libraries can beadjusted to meet the needs of an animator. I plan to achievethis flexibility by decomposing a character's motion into afundamental component and a style component in a waythat allows us to independently transfer these components tonew characters. For example, the animator can produce achild's cheerful run sequence by starting with a runningmotion library extracted from the captured human run. Thismotion can be modified by applying a happy, exuberant motionstyle, which is then transferred to an animated character ofa small child. Such decoupling of the fundamental motion,style, and performing character provides an extremelypowerful and flexible animation paradigm that can produce awide range of animations from a very small motion dataset.The main goal of the educational component of this proposalis to demonstrate the utility of advanced animation tools ineducation and story-telling. I am particularly interested inintroducing these new animation tools to Seattle high-schoolstudents to verify that our research does indeed empowerevery story-teller with an accessible medium of expression.I am also planning a project-oriented animation courseintended for undergraduate and graduate students involved inanimation research, as well as in art and music. Together,the students will work on using our tools to create anartistic short film project. This synergistic effort willexpose the artists to the latest animation technology andprovide direct user feedback to students involved inresearch. In addition, the created motion libraries as wellas the animation tools, will be freely distributed throughthe web and CDs with the hope that they will help animationbecome an effective communication medium across educational,economic and cultural boundaries.
动画是一种非常强大的表达媒介,那些复杂的甚至无法用静止的图像或文字来表达的思想,都可以通过动画片段轻松地表达出来。不幸的是,即使通过动画传达的概念很简单,制作动画的过程也异常困难。事实上,这也是电脑动画变得如此占主导地位的原因之一--它有望实现自动化,从而大大简化制作过程。可悲的是,将想法转化为电脑动画的过程还远不简单。例如,动画长片《玩具总动员2》需要200多名高技能的计算机和动画专家花三年多的时间进行极其艰苦的工作。我的职业目标是开发概念和算法,快速发展的现实主义动画,有效地传达创作者的目的。这项研究希望创造的理论和实验基础,建立更强大的动画工具,比存在于今天。这些工具将大大减少熟练的动画师所需的制作时间,更重要的是,使普通计算机用户能够通过动画表达他们的想法。这些工具也将帮助教育者更有效地教学,并允许我们每个人成为个人和虚构故事的创作者和导演。只有当动画制作更容易的时候,动画才会像今天的图像和文本一样在网络上司空见惯。此外,直观的方法来创建逼真的人体运动将进一步使人类化身的远程呈现应用程序,新的拟人化人机界面,和现实的数字演员在故事片和视频游戏的创建。计算机动画将不会实现其全部潜力,直到1)动画工具需要很少或根本没有技能,2)动画师的创造力不会被动画工具的限制所扼杀,3)动画系统可供每个人使用电脑和讲故事。我很高兴能为这些目标做出重大贡献。这项研究的核心是观察到动画师的创造力只有在制作出逼真的动作序列变得更容易时才能得到充分的实现。这个问题是非常具有挑战性的,因为运动的基本物理模型很难创建和控制。我相信解决方案在于创建高度灵活的逼真运动库,以及直观和广泛地修改它们的工具。库的强大之处在于它能够重用已经存在的高质量运动,从而减少对动画技能的需求。在这种范式中,动画过程变成了选择特定的运动库,并修改一组运动属性,将原始运动转换为最终动画。在我的研究中,我建议开发一种创建和使用逼真运动库的方法,该方法足够灵活,可以在广泛的应用中使用。在1995年,我提出了使用运动变换作为一种新的方法来创建动画-一种从根本上不同于传统的从头开始创建动画的方法。这种变换方法特别适用于修改从真实世界演员捕获的逼真运动数据。不幸的是,在转型过程中,许多现实主义倾向于丢失。最近,我发现了一种新的转换方法,它表明动画可以直观地转换成各种新的序列,而不会违反运动的基本动力学特性。通过维护动画角色的动态和生物力学特性的模型来保持真实感。在未来,我将进一步开发可以与真实世界数据相结合的数学模型,以创建可重用的情感库。除了保持运动的真实感之外,对有效的运动库最重要的要求是库可以被调整以满足动画师的需求的灵活性。我计划通过将角色的动作分解为基本成分和风格成分来增加这种灵活性,这样我们就可以独立地将这些成分转移到新的角色身上。例如,动画制作者可以通过从捕捉到的人类跑步中提取的跑步运动库来制作孩子欢快的跑步序列。这种运动可以通过应用一个快乐的,旺盛的运动风格来修改,然后将其转移到一个小孩子的动画角色上。这种基本运动、风格和表演角色的分离提供了一个非常强大和灵活的动画范例,可以从一个非常小的运动数据集产生各种各样的动画。这个提议的教育部分的主要目标是展示先进的动画工具在教育和讲故事方面的效用。我特别感兴趣的是向西雅图的高中生介绍这些新的动画工具,以验证我们的研究确实能让每个讲故事的人都有一个可访问的表达媒介。我还计划为参与动画研究以及艺术和音乐的本科生和研究生开设一门以项目为导向的动画课程。学生们将一起使用我们的工具来创建一个艺术短片项目。这种协同努力将使艺术家们接触到最新的动画技术,并为参与研究的学生提供直接的用户反馈。此外,创建的运动库以及动画工具将通过网络和CD免费分发,希望它们将有助于动画成为跨越教育,经济和文化界限的有效传播媒介。

项目成果

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Zoran Popovic其他文献

Brain points: a growth mindset incentive structure boosts persistence in an educational game
大脑要点:成长型思维激励结构可提高教育游戏的持久性
Percutaneous transarterial balloon dilatation of the mitral valve: five year experience.
经皮经动脉二尖瓣球囊扩张术:五年经验。
  • DOI:
    10.1136/hrt.67.2.185
  • 发表时间:
    1992
  • 期刊:
  • 影响因子:
    0
  • 作者:
    U. Babic;S. Grujicic;Zoran Popovic;Z. Djurisic;P. Pejcic;Mihailo Vučinić
  • 通讯作者:
    Mihailo Vučinić
Proactive Sensing for Improving Hand Pose Estimation
用于改进手部姿势估计的主动传感
Generalizing locomotion style to new animals with inverse optimal regression
通过逆最优回归将运动方式推广到新动物
  • DOI:
  • 发表时间:
    2014
  • 期刊:
  • 影响因子:
    6.2
  • 作者:
    K. Wampler;Zoran Popovic;J. Popović
  • 通讯作者:
    J. Popović
PROGNOSTIC VALUE OF ECHO-DOPPLER GUIDED AV DELAY OPTIMIZATION FOLLOWING CARDIAC RESYNCHRONIZATION THERAPY
  • DOI:
    10.1016/s0735-1097(16)31492-9
  • 发表时间:
    2016-04-05
  • 期刊:
  • 影响因子:
  • 作者:
    Srikanth Koneru;Zoran Popovic;Paul Cremer;Patrick Tchou;Bruce Wilkoff;Bruce Lindsay;Brian Griffin;Richard Grimm
  • 通讯作者:
    Richard Grimm

Zoran Popovic的其他文献

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{{ truncateString('Zoran Popovic', 18)}}的其他基金

Investigating the Effects of Computational Thinking Games on Mathematical and Scientific Practices
研究计算思维游戏对数学和科学实践的影响
  • 批准号:
    1639576
  • 财政年份:
    2016
  • 资助金额:
    $ 32.5万
  • 项目类别:
    Standard Grant
CI-EN: Collaborative Research: Enhancement of Foldit, a Community Infrastructure Supporting Research on Knowledge Discovery Via Crowdsourcing in Computational Biology
CI-EN:协作研究:Foldit 的增强,Foldit 是一个支持计算生物学中通过众包进行知识发现研究的社区基础设施
  • 批准号:
    1625811
  • 财政年份:
    2016
  • 资助金额:
    $ 32.5万
  • 项目类别:
    Standard Grant
BIGDATA: F: BCC: Data driven optimization of classroom learning activities
BIGDATA:F:BCC:数据驱动的课堂学习活动优化
  • 批准号:
    1546510
  • 财政年份:
    2015
  • 资助金额:
    $ 32.5万
  • 项目类别:
    Standard Grant
Eager: Large Scale Neuron Reconstruction through Development of Crowdsourced Reconstruction Experts
Eager:通过众包重建专家的发展进行大规模神经元重建
  • 批准号:
    1551063
  • 财政年份:
    2015
  • 资助金额:
    $ 32.5万
  • 项目类别:
    Standard Grant
HCC: Large: Collaborative Research: DNA Machine Builder: Creative molecular-machine design through mass-scale crowdsourcing
HCC:大型:协作研究:DNA Machine Builder:通过大规模众包进行创意分子机器设计
  • 批准号:
    1212940
  • 财政年份:
    2012
  • 资助金额:
    $ 32.5万
  • 项目类别:
    Standard Grant
HCC-Small: Protein Design Through Massively Distributed Video Games
HCC-Small:通过大规模分布式视频游戏进行蛋白质设计
  • 批准号:
    0811902
  • 财政年份:
    2008
  • 资助金额:
    $ 32.5万
  • 项目类别:
    Standard Grant
Surface and Motion Capture for High Fidelity Synthesis of Digital Humans
用于数字人高保真合成的表面和运动捕捉
  • 批准号:
    0098005
  • 财政年份:
    2001
  • 资助金额:
    $ 32.5万
  • 项目类别:
    Continuing Grant

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