智能虚拟环境中虚拟人情感仿真研究
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摘要
情感计算是一个新兴的研究领域,它通过赋予计算机识别、理解、表达和适应用户情感的能力来建立和谐的人机环境,使计算机具有更高更全面的智能。情感计算可分为情感获取、情感生成和情感表达三个方面。本文的工作侧重于研究虚拟人情感的生成和表达。为了更专注于虚拟人的情感建模,系统应该提供一种智能的虚拟环境以便屏蔽掉虚拟人构建的物理细节,同时能够对虚拟人的高层交互行为做出反应。本文采用基于语义的方法来构建这种智能虚拟环境(IVE)。
在虚拟人的情感建模方面,目前的情感模型多采用认知的方法构建,没有考虑到人类的本能情感反应,此外对于人类情感的不确定性的模拟仍不成熟。本文提出多刺激下非确定性情感生成器来解决这些问题。利用粒子采样的方法对非确定情感进行抽样,用认知来修正抽样结果,这样将情感用粒子的加权值来表示,从而将人类的基本情感和复杂情感结合起来并构建了非确定的情感过程。
为了构建智能虚拟人,仅有情感模型并不够,必须对虚拟人的情感进行外在的表现。本文同时兼顾人脸表情输出和人体动作输出,在人脸表情输出方面构建了六种基本的面部表情,根据具有多种情感的混合情感状态中各基本情感所占的比例进行表情的混合插值；在人体情感动作表达方面,将人体动作分为不同人体部位的运动片段,并标记这些片段之间的时空约束关系,在创建情感动作时选择具有相关情感的片段同时输出,即多模情感表达序列的构建。
虚拟人群组的情感仿真也是本文的研究重点。针对虚拟人群情感传递的特性,为降低情感传递的复杂度进而提升计算时间,本文提出了采用二维热传导模型进行人群情感模拟的算法并给出了仿真结果。对于二维的热传导模型,采用交替方向隐式差分方法(AD1)计算结果,并利用统一计算架构(CUDA)进行算法的并行化处理。实验表明,该算法针对千人规模的虚拟人群可达到实时效果。
Affective computing is an emerging research field. It constructs a harmonious human-machine environment and improves the computer intelligence by enabling computer to recognize, understand,express and adapt to the user's emotions. Affective computing can be divided into affect detection, emotion modeling and emotion expression. We lay our efforts mainly on emotion generation and expression of virtual human. In order to focus on emotion modeling, intelligent virtual environments should be constructed that it can shield off the physical details of virtual human construction and react to high-level virtual human interaction. We adopted semantic based methods to build this kind of intelligent virtual environments (IVEs).
In virtual human emotion modeling fields, the current emotional models are basically ground on cognition so that they omit the affect of basic human emotions. In addition, the simulation of uncertain human emotions is still a challenge. We provided uncertain emotion generator under multi-stimulus to solve these problems. We used particles to sample the uncertain emotions and. modified these samples by cognition. Then human emotions can be represented by the weighted values of these particles. In this way, we combined basic human emotions and complex emotions together and constructed uncertain emotion process.
Only emotion model is not enough to construct believable virtual human. Emotions must be presented outside through their bodies. We built facial expressions and body behaviors separately. In facial expression fields, we built six basic facial expressions and blended them according to the probability of basic emotions in the mixed emotional states. In human emotional behavior construction aspect, we divided the human actions into movement fragments of different body parts, then labeled these fragments manually to record the temporal and spatial constrains, at last we combined the fragments of different body parts to form multi-model emotion expression sequence.
Emotion transmission in virtual crowds is our research focus. According to the features of crowds emotion propagation and for the sake of reducing the computing time, we proposed that heat conduction model can be used to simulate virtual crowds emotion transmission. We adopted alternating direction implicit (ADI) method to calculate the difference results and compute unified device architecture (CUDA) platform to accelerate the speed. We can run a real-time crowds simulation for thousand scale.
在虚拟人的情感建模方面,目前的情感模型多采用认知的方法构建,没有考虑到人类的本能情感反应,此外对于人类情感的不确定性的模拟仍不成熟。本文提出多刺激下非确定性情感生成器来解决这些问题。利用粒子采样的方法对非确定情感进行抽样,用认知来修正抽样结果,这样将情感用粒子的加权值来表示,从而将人类的基本情感和复杂情感结合起来并构建了非确定的情感过程。
为了构建智能虚拟人,仅有情感模型并不够,必须对虚拟人的情感进行外在的表现。本文同时兼顾人脸表情输出和人体动作输出,在人脸表情输出方面构建了六种基本的面部表情,根据具有多种情感的混合情感状态中各基本情感所占的比例进行表情的混合插值；在人体情感动作表达方面,将人体动作分为不同人体部位的运动片段,并标记这些片段之间的时空约束关系,在创建情感动作时选择具有相关情感的片段同时输出,即多模情感表达序列的构建。
虚拟人群组的情感仿真也是本文的研究重点。针对虚拟人群情感传递的特性,为降低情感传递的复杂度进而提升计算时间,本文提出了采用二维热传导模型进行人群情感模拟的算法并给出了仿真结果。对于二维的热传导模型,采用交替方向隐式差分方法(AD1)计算结果,并利用统一计算架构(CUDA)进行算法的并行化处理。实验表明,该算法针对千人规模的虚拟人群可达到实时效果。
Affective computing is an emerging research field. It constructs a harmonious human-machine environment and improves the computer intelligence by enabling computer to recognize, understand,express and adapt to the user's emotions. Affective computing can be divided into affect detection, emotion modeling and emotion expression. We lay our efforts mainly on emotion generation and expression of virtual human. In order to focus on emotion modeling, intelligent virtual environments should be constructed that it can shield off the physical details of virtual human construction and react to high-level virtual human interaction. We adopted semantic based methods to build this kind of intelligent virtual environments (IVEs).
In virtual human emotion modeling fields, the current emotional models are basically ground on cognition so that they omit the affect of basic human emotions. In addition, the simulation of uncertain human emotions is still a challenge. We provided uncertain emotion generator under multi-stimulus to solve these problems. We used particles to sample the uncertain emotions and. modified these samples by cognition. Then human emotions can be represented by the weighted values of these particles. In this way, we combined basic human emotions and complex emotions together and constructed uncertain emotion process.
Only emotion model is not enough to construct believable virtual human. Emotions must be presented outside through their bodies. We built facial expressions and body behaviors separately. In facial expression fields, we built six basic facial expressions and blended them according to the probability of basic emotions in the mixed emotional states. In human emotional behavior construction aspect, we divided the human actions into movement fragments of different body parts, then labeled these fragments manually to record the temporal and spatial constrains, at last we combined the fragments of different body parts to form multi-model emotion expression sequence.
Emotion transmission in virtual crowds is our research focus. According to the features of crowds emotion propagation and for the sake of reducing the computing time, we proposed that heat conduction model can be used to simulate virtual crowds emotion transmission. We adopted alternating direction implicit (ADI) method to calculate the difference results and compute unified device architecture (CUDA) platform to accelerate the speed. We can run a real-time crowds simulation for thousand scale.
引文
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