摘要： 为解决传统逆向运动学算法计算繁琐、效果不逼真的问题，提出一种快速自适应比例高斯过程隐变量模型(FASGPLVM)，并基于该模型实现人体运动生成。实验结果表明，FASGPLVM模型具有较快的收敛速度和收敛精度，能自适应运动编辑的方向，扩大运动捕获数据的可编辑幅度。

关键词: 高斯过程, 隐变量模型, 核函数, 运动生成

Abstract: In order to void cockamamie computation and pose distortion existing in traditional inverse kinematics, this paper presents a Fast Adaptive Scaled Gaussian Process Latent Variable Model(FASGPLVM), then realizes human motion generation based on it. Experimental results show that FASGPLVM has higher convergence velocity and precision and extends editing range of motion capture data by adapting motion editing direction.

Key words: Gaussian process, latent variable model, kernel function, motion generation

中图分类号: 

• TP391