CASIA OpenIR  > 毕业生  > 博士学位论文
人脸表情合成技术研究
Alternative TitleResearch on Facial Expression Synthesis
杜志军
Subtype工学博士
Thesis Advisor王阳生
2010-05-31
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline计算机应用技术
Keyword表情合成 人脸建模与动画 表情映射 眼睛处理 Facial Expression Synthesis Face Modelling And Animation Expression Mapping Eye Processing
Abstract人脸表情合成是新一代人机交互中的重要技术,也是当前活跃的研究方向,在计算机图形学和计算机视觉界都得到广泛的关注。其在视频传输、计算机辅助教学、影视制作、虚拟现实等领域有着重要的应用价值。本文分别从数字图像处理、三维人脸建模和人机交互的角度出发,对人脸表情合成技术进行了研究,主要工作和贡献如下: 1. 提出一种基于主动外观模型的表情合成方法。本文利用主动外观模型实现对人脸的参数化描述。通过对大量表情样本的分析,总结出了人脸形状和纹理变化的特点。通过神经网络训练出了人脸形状的映射函数,利用表情强度与纹理的关系建立了一个线性纹理映射函数。利用训练出的映射函数可以合成出不同表情强度的表情图像。 2. 提出一种基于梯度域的表情合成技术。针对传统表情比例图方法对光照敏感的缺点,提出在梯度域实现人脸表情的映射。通过梯度场混合技术将源表情图像中的褶皱信息迁入到目标人脸中,增强了抗光照的能力。为了增强合成图像的动感,提出了基于纹理合成的眼睛处理方法,实现了眼睛运动的模拟;引入三维人脸模型辅助实现了头部姿态的改变。 3. 提出一种基于人脸建模的表情动画方法。对于给定的单张人脸图像,自动标定图像中关键点的位置,利用图像中的特征点和人脸形变模型迭代出个性化的人脸模型。为了建立完整的人脸模型,另外添加了眼睛、牙齿和背景网格。采用基于径向基函数的表情映射技术将源模型上的稀疏动画数据映射到个性化人脸模型上,结合球面参数化实现动画数据的插值。为了提高动画的逼真感,对眼睛部分作了单独处理。 4. 提出一种基于表演驱动的表情合成技术。利用改进的AAM算法跟踪表演者脸上18个特征点的位置信息,利用这些特征点间的距离来表示人脸的动作参数。通过离线学习建立动作参数与目标人脸表情的对应关系,并针对每个人脸区域的特点提出了相应的表情驱动方法。
Other AbstractRealistic facial expression synthesis appears as an important technique in the field of human computer interaction. It is also an active research topic both in computer vision and computer graphics community. The potential application of this technique includes low bit-rate video transmission, computer aided instruction, film design, virtual reality and so on. In this dissertation, we study on facial expression synthesis technique from three aspects: image-based, face modeling-based and video driven based. The main contributions of this thesis are as follows: 1. A facial expression synthesis method based on Active Appearance Model is proposed. Active Appearance Model is used to represent faces as a few of parameters. Through analysis of large samples of facial expressions, we obtain the characteristic of changes in shape and texture during expressions. We adopt neural network to train the mapping functions for facial shape, and use the relationship between expression intensity and facial texture to create a mapping function for facial texture. Finally, facial expressions with different intensity can be synthesized by these mapping functions. 2. A gradient-domain based facial expression synthesis method is proposed. Since the expression ratio image method is sensitive to illumination, we propose to fulfill the facial expression mapping in gradient domain. Wrinkles on source expressive face is transferred to target face by mixing gradients of both images, so this method is robust to illumination. To generate dynamic expressions, an eye processing technique based on texture synthesis is presented and a three-dimensional face model is used to change the head pose. 3. A face modeling and animation technique is proposed. Given a single face image, feature points on the face are labeled automatically, then by minimizing the distance between feature points on the 3D morphable model and face image, an individual face model is generated. In order to create a model including all facial organs, other meshes like eye, tooth and background are added. To animate the generated face model, first, sparse motion data of a source model is transferred to the target model by Radius Basis Function. Then, sphere parameterization is used to calculate the barycentric coordinate for interpolation. In addition, the eye model is processed independently to enhance realism of the animation. 4. A performance-driven facial expression synthesis method is proposed. An improved AAM algor...
shelfnumXWLW1517
Other Identifier200718014629088
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/6273
Collection毕业生_博士学位论文
Recommended Citation
GB/T 7714
杜志军. 人脸表情合成技术研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2010.
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