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KeyFrame extraction for human motion capture data via multiple binomial fitting
Computer Animation and Virtual Worlds ( IF 1.1 ) Pub Date : 2020-12-14 , DOI: 10.1002/cav.1976
Chenxu Xu 1 , Wenjie Yu 1 , Yanran Li 2 , Xuequan Lu 3, 4 , Meili Wang 1, 5 , Xiaosong Yang 2
Affiliation  

In this paper, we make two contributions. The first is to propose a new keyframe extraction algorithm, which reduces the keyframe redundancy and reduces the motion sequence reconstruction error. Secondly, a new motion sequence reconstruction method is proposed, which further reduces the error of motion sequence reconstruction. Specifically, we treated the input motion sequence as curves, then the binomial fitting was extended to obtain the points where the slope changes dramatically in the vicinity. Then we took these points as inputs to obtain keyframes by density clustering. Finally, the motion curves were segmented by keyframes and the segmented curves were fitted by binomial formula again to obtain the binomial parameters for motion reconstruction. Experiments show that our methods outperform existing techniques, in terms of reconstruction error.

中文翻译:

通过多个二项式拟合来提取人体运动捕获数据的关键帧

在本文中,我们做出了两个贡献。首先是提出一种新的关键帧提取算法,该算法减少了关键帧冗余并减少了运动序列重构误差。其次,提出了一种新的运动序列重建方法,进一步减少了运动序列重建的误差。具体来说,我们将输入运动序列视为曲线,然后将二项式拟合扩展以获取斜率在附近急剧变化的点。然后,我们将这些点作为输入,以通过密度聚类获得关键帧。最后,通过关键帧对运动曲线进行分割,再通过二项式公式对分割后的曲线进行拟合,得到用于运动重构的二项式参数。实验表明,我们的方法优于现有技术,
更新日期:2020-12-14
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