Real-time labeling of non-rigid motion capture marker sets

被引:10
作者
Alexanderson, Simon [1 ]
O'Sullivan, Carol [2 ]
Beskow, Jonas [1 ]
机构
[1] KTH Speech Mus & Hearing, Lindsetdtsv 24, S-10044 Stockholm, Sweden
[2] Trinity Coll Dublin, Coll Green, Dublin 1, Ireland
来源
COMPUTERS & GRAPHICS-UK | 2017年 / 69卷
基金
爱尔兰科学基金会;
关键词
Animation; Motion capture; Hand capture; Labeling; ALGORITHM;
D O I
10.1016/j.cag.2017.10.001
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Passive optical motion capture is one of the predominant technologies for capturing high fidelity human motion, and is a workhorse in a large number of areas such as bio-mechanics, film and video games. While most state-of-the-art systems can automatically identify and track markers on the larger parts of the human body, the markers attached to the fingers and face provide unique challenges and usually require extensive manual cleanup. In this work we present a robust online method for identification and tracking of passive motion capture markers attached to non-rigid structures. The method is especially suited for large capture volumes and sparse marker sets. Once trained, our system can automatically initialize and track the markers, and the subject may exit and enter the capture volume at will. By using multiple assignment hypotheses and soft decisions, it can robustly recover from a difficult situation with many simultaneous occlusions and false observations (ghost markers). In three experiments, we evaluate the method for labeling a variety of marker configurations for finger and facial capture. We also compare the results with two of the most widely used motion capture platforms: Motion Analysis Cortex and Vi con Blade. The results show that our method is better at attaining correct marker labels and is especially beneficial for real-time applications. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:59 / 67
页数:9
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