人体运动的函数数据分析与合成

在许多虚拟现实的应用中,虚拟人作为人在计算机中的表示,是提高其交互能力和沉浸感的重要因素之一.然而,对于虚拟人建模而言,合成逼真、可控的虚拟人运动仍然是具有挑战性的课题.为此,提出了一种基于函数数据分析的人体运动合成方法.通过对一组样本运动进行函数主成分分析,构建出一个由特征运动构成的低维函数子空间.该低维子空间不仅能够有效地刻画样本序列内在的变化规律,而且也为有目的的运动合成提供了方法.在该空间中,通过控制各特征运动的系数即可合成出逼真、平滑的运动序列.该合成过程没有耗时的计算,因此能够满足各种实时应用

Motion Synthesis for Virtual Human Using Functional Data Analysis

In many virtual reality applications the virtual human, as the digital representation of human, is one of the most important elements to improve the interactive capability and immersive experience. However, it remains a challenge for modeling virtual human to synthesize natural and controllable motions. This paper presents a novel method for motion synthesis based on functional data analysis. A low-dimensional functional space is constructed from a set of example motions by using functional principal components analysis. This functional space can not only discover the true dimension of the examples, but also provide an approach to synthesize natural and smooth motions with purpose by controlling the coefficients of each functional basis. This synthesis process is very efficient because there is no time-consuming calculation, which can meet the requirement of real-time applications. The experiments have proven the robustness and effectiveness of this method.