Motion capture is increasingly used in games and movies, but often requires editing before it can be used, for many reasons. The motion may need to be adjusted to correctly interact with virtual objects or to fix problems that result from mapping the motion to a character of a different size or, beyond such technical requirements, directors can request stylistic changes. Unfortunately, editing is laborious because of the low-level representation of the data. While existing motion editing methods accomplish modest changes, larger edits can require the artist to “re-animate” the motion by manually selecting a subset of the frames as keyframes. In this paper, we automatically find sets of frames to serve as keyframes for editing the motion. We formulate the problem of selecting an optimal set of keyframes as a shortest-path problem, and solve it efficiently using dynamic programming. We create a new simplified animation by interpolating the found keyframes using a naive curve fitting technique. Our algorithm can simplify motion capture to around 10% of the original number of frames while retaining most of its detail. By simplifying animation with our algorithm, we realize a new approach to motion editing and stylization founded on the time-tested keyframe interface. We present results that show our algorithm outperforms both research algorithms and a leading commercial tool.

中文翻译：

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动态捕捉的最佳和交互式关键帧选择

运动捕捉越来越多地用于游戏和电影中，但是出于许多原因，运动捕捉通常需要进行编辑才能使用。可能需要调整该动作以正确地与虚拟对象进行交互，或者解决由于将动作映射到其他大小的字符而导致的问题，或者超出此类技术要求，导演可以要求进行样式更改。不幸的是，由于数据的底层表示，编辑很费力。现有的动作编辑方法可以完成适度的更改，而较大的编辑可能需要美术师通过手动选择帧的子集作为关键帧来“重新制作动画”。在本文中，我们自动找到框架集作为用作编辑运动的关键帧。我们提出选择最优的问题集关键帧作为最短路径问题，并使用动态编程有效地解决它。通过使用朴素曲线拟合技术对找到的关键帧进行插值，我们创建了一个新的简化动画。我们的算法可以将运动捕捉简化为原始帧数的大约10％，同时保留其大部分细节。通过使用我们的算法简化动画，我们实现了在经过时间检验的关键帧界面基础上进行运动编辑和样式化的新方法。我们提供的结果表明我们的算法优于研究算法和领先的商业工具。