A Generative Model for Motion Synthesis and Blending Using Probability Density Estimation
A Generative Model for Motion Synthesis and Blending Using Probability Density Estimation
复制标题
使用概率密度估计进行运动合成和混合的生成模型
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
R. Bowden
中科院分区：
文献类型：
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作者：
Dumebi Okwechime;R. Bowden
本文的重点是提出一种通过学习运动生成模型来重用运动捕获数据的方法。该模型允许合成和混合循环运动，同时为其提供原始数据中存在的风格和现实感。这是通过将数据投影到较低维空间并学习运动序列的多变量概率分布来实现的。作为产生式模型，概率密度估计被用来从模型中产生新的运动，基于梯度的优化被用于生成最终的动画。结果显示，不同动作之间合理的动作生成和逼真的混合。
The main focus of this paper is to present a method of reusing motion captured data by learning a generative model of motion. The model allows synthesis and blending of cyclic motion, whilst providing it with the style and realism present in the original data. This is achieved by projecting the data into a lower dimensional space and learning a multivariate probability distribution of the motion sequences. Functioning as a generative model, the probability density estimation is used to produce novel motions from the model and gradient based optimisation used to generate the final animation. Results show plausible motion generation and lifelike blends between different actions.