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An Efficient Keyframe Extraction from Motion Capture Data

机译:从运动捕获数据中提取有效的关键帧提取

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This paper proposes a keyframe extraction method based on a novel layered curve simplification algorithm for motion capture data. Bone angles are employed as motion features and keyframe candidates can be selected based on them. After that, the layered curve simplification algorithm will be used to refine those candidates and the keyframe collection can be gained. To meet different requirements for compression and level of detail of motion abstraction, adaptive extraction parameters are also applied. The experiments demonstrate that our method can not only compress and summarize the motion capture data efficiently, but also keep the consistency of keyframe collection between similar human motion sequences, which is of great benefit to further motion data retrieval or editing.
机译:本文提出了一种基于新型分层曲线简化算法的关键帧提取方法,用于运动捕获数据。骨角被采用作为运动特征,并且可以基于它们选择关键帧候选。之后,分层曲线简化算法将用于改进那些候选者并且可以获得关键帧收集。为了满足不同要求的压缩和运动抽象细节水平,还应用了自适应提取参数。实验表明,我们的方法不仅可以高效地压缩和总结运动捕获数据,而且还保持相似人体运动序列之间的关键帧集合的一致性,这对于进一步的运动数据检索或编辑具有很大的益处。

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