Generalizing motion edits with Gaussian processes
Generalizing motion edits with Gaussian processes
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DOI：
10.1145/1477926.1477927
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发表时间：
2009-02
期刊：
影响因子：
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通讯作者：
L. Ikemoto;Okan Arikan;D. Forsyth
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文献类型：
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作者：
L. Ikemoto;Okan Arikan;D. Forsyth
艺术家创建引人注目的角色动画的一种方法是操纵角色运动的细节。这个过程是昂贵和重复的。我们表明，我们可以使这样的运动编辑更有效地概括的编辑动画制作的短序列的运动到其他序列。我们的方法预测帧的运动使用高斯过程模型的运动学和动力学。这些估计与概率推断相结合。我们的方法可用于将编辑从示例传播到现有角色的整个序列，也可用于将运动从控制角色映射到非常不同的目标角色。该技术具有良好的推广性。例如，我们证明了一个估计器，从几秒钟的编辑示例动画使用我们的方法，概括不够好，以高质量的方式编辑分钟的角色动画。学习是互动的：想要改进输出的动画师可以提供小的、正确的示例，并且系统将产生改进的运动估计。我们使用具有新颖功能的快速全身IK系统，使这种交互式学习过程高效而自然。最后，我们提出的数据，从采访专业的角色动画师，表明推广和宣传动画编辑可以节省艺术家显着的时间和工作。
One way that artists create compelling character animations is by manipulating details of a character's motion. This process is expensive and repetitive. We show that we can make such motion editing more efficient by generalizing the edits an animator makes on short sequences of motion to other sequences. Our method predicts frames for the motion using Gaussian process models of kinematics and dynamics. These estimates are combined with probabilistic inference. Our method can be used to propagate edits from examples to an entire sequence for an existing character, and it can also be used to map a motion from a control character to a very different target character. The technique shows good generalization. For example, we show that an estimator, learned from a few seconds of edited example animation using our methods, generalizes well enough to edit minutes of character animation in a high-quality fashion. Learning is interactive: An animator who wants to improve the output can provide small, correcting examples and the system will produce improved estimates of motion. We make this interactive learning process efficient and natural with a fast, full-body IK system with novel features. Finally, we present data from interviews with professional character animators that indicate that generalizing and propagating animator edits can save artists significant time and work.