Abstract
Key-frame extracting technology has been widely used in the field of human motion synthesis. Efficient and accurate key frames extraction methods can improve the accuracy of motion synthesis. In this paper, we use an optimized t-Stochastic Neighbor Embedding (t-SNE for short) algorithm to reduce the data and on this basis extract the key frames. The experimental results show that the validity of this method is better than the existing methods under the same experimental data.
Highlights
Intelligent algorithms for synthesizing human motion capture data are useful in many fields such as entertainment, education, training simulators and so on
Key-frame extracting technology has been widely used in many aspects of human motion synthesis
The translation of joint root determines the current position of the skeleton while the rotation of joint root determines the overall orientation of the skeleton
Summary
Intelligent algorithms for synthesizing human motion capture data are useful in many fields such as entertainment, education, training simulators and so on. One synthesizes a motion from “going” to “running”, the intermediate state of two typical movements is similar and the transitional frames which satisfied the threshold may appear in the time when the feet are off the ground. Under this circumstance, the transitional motion appears in the substandard physical laws position so that the result of motion syntheses is serious distortion. The transitional motion appears in the substandard physical laws position so that the result of motion syntheses is serious distortion To prevent this problem, we need to extract key-frame and calculate similarity based upon those motions. In order to prove the validity of this algorithm, we analyzed and contrasted the results by comparative experiments
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