特定人脸的快速定制和肌肉驱动的表情动画

为解决特定人脸的真实模拟问题提供了一个简单而行之有效的方法,首先,给出特定人脸的正侧面正交图像,并提供了一个内嵌肌肉向量的一般人脸三维多边形网络模型,采用Snake技术自动配人脸特征线,基于特定人脸的特征线相对于一般人脸模型上的特征线的位移,变分插值一般人脸网络,适配特定人脸几何;然后,用多分辨率样条技术产生无缝的人脸纹理镶嵌图,纹理映射后生成高度真实感的能以任意视线方向观察的特定人脸;进而,通过组合特定人脸的肌肉向量的运动,变形模型,组合出特定人脸的各种表情,该方法能在廉价的PC平台上实现,快速、简单,而且具有真实感。     

利用双线性分析的三维人脸表情模拟技术

具有真实感的三维人脸表情合成是计算机应用领域的一个热点问题.提出一种基于双线性分析的三维人脸表情生成方法.在对人脸数据进行分区域统计分析的基础上,建立了表情与身份相独立的双线性统计模型.设计了该模型的肌肉驱动方法,通过肌肉参数来驱动相应表情统计参数变化来生成丰富表情.对于输入的特定二维或三维人脸,利用形变模型(Morphable Model), 可自动实现其模型匹配.实验结果表明,该方法能够模拟各种具有较高真实感的人脸表情.     

一个基于正交图像重建虚拟人脸的快速方法

文中以较少的交互 ,给出克隆三维虚拟人脸的方法。即首先根据两幅人脸图像 ,对一般人脸模型进行整体变换 ,然后基于特定人脸的特征线相对于一般人脸模型上的特征线的位移 ,用变分插值技术变分一般人脸网格 ,适配特定人脸几何。最后用多分辨率样条技术产生无缝的人脸纹理镶嵌图(Mosaic) ,并对整个人脸模型进行纹理映射 ,从而生成高度真实感的能以任意视线方向观察的特定人脸。     

面向纹理特征的真实感三维人脸动画方法

纹理变化是人脸表情的重要组成部分，传统的人脸动画方法通常只是对纹理图像做简单的拉伸变换，没有考虑人脸细微纹理特征的变化，比如皱纹、酒窝等，该文提出了一种面向纹理特征变化的真实感三维人脸动画方法．给出局部表情比率图(Partial Expression Ratio Image，PERI)的概念及其获取方法，在此基础上，进一步给出了面向MPEG-4的PERI参数化与面向三维人脸动画的多方向PERI方法，前者通过有机结合MPEG-4的人脸动画参数(Facial Anlmation Parameter，FAP)，实现人脸动画中细微表情特征的参数化表示；后者通过多方向PERI纹理特征调整方法，使得三维人脸模型在不同角度都具有较好的细微表情特征,该文提出的方法克服了传统人脸动画只考虑人脸曲面形变控制而忽略纹理变化的缺陷，实现面向纹理变化的具有细微表情特征的真实感三维人脸动画，实验表明，该文提出的方法能有效捕捉纹理变化细节，提高人脸动画的真实感。     

基于二维图像的三维人脸建模技术

建立三维人脸模型和表情动画是计算机图形学领域的一个研究热点。文章提出了一种基于二维图像的三维人脸建模方法,首先在给定的人脸的正侧面照片上提取事先定义好的反映人脸特征的特征点信息,与一个一般人脸模型上对应点的信息进行比较和修改,得到反映给定人脸特征的特定人脸模型。最后使用纹理映射技术给特定人脸模型添加纹理信息,形成真实感的虚拟三维人脸模型。     

人脸建模和动画的基本技术

文中讨论了通过人脸的正向和侧面照片构造头部的三维模型,并赋予其简单动画的基本技术,基于此技术,实现了一个交互式人脸建模和动画的工具,详细阐述了在系统实现过程中应用到的人脸几何表示,由一般人脸变化到特定人脸,弹性网格,肌肉模型,关键,帧和表情提取等技术。     

视频驱动人脸动画合成

描述了一种实时的视频驱动的人脸动画合成系统.通过该系统,用户只要在摄像头前面表演各种脸部动作,就可以控制虚拟人脸的表情.首先,建立一个基于肌肉的三维人脸模型,并使用肌肉激励参数控制人脸形变.为提高人脸动画的真实感,将口轮匝肌分解为外圈和内圈两部分,同时建立脸部形变与下颌转动的关系.然后,使用一种实时的特征点跟踪算法跟踪视频中人脸的特征点.最后,将视频跟踪结果转换为肌肉激励参数以驱动人脸动画.实验结果表明,该系统能实时运行,合成的动画也具有较强真实感.与大部分现有的视频驱动的人脸动画方法相比,该系统不需要使用脸部标志和三维扫描设备,极大地方便了用户使用.     

虚拟特定人脸的表情合成技术

通过对一般人脸网格的变形和纹理映射，使得虚拟人脸与真实人脸达到了形似；通过对人脸面部肌肉分布的研究，运用肌肉驱动技术合成了人脸的表情，使得虚拟人脸与真实人脸达到神似；从而合成了具有真实感的形神兼备的虚拟人脸。     

人脸面部表情自动生成系统

详细介绍开发一个人脸面部表情自动生成系统所用到的相关技术,包括人脸三维建模、面部表情合成、系统用户界面实现等技术。系统开发的目的是建立一个具有真实感和实用性的三维人脸面部表情自动生成系统,其中,三维人脸建模技术包括3DMAX使用关键技术以及人脸特征提取技术,基于人脸建模的表情生成技术主要介绍通过肌肉运动来描述表情的技术,系统界面的设计采用MFC技术。对各技术全面而系统的介绍,将为人脸面部表情自动生成系统的开发与实现提供一个有效途径,为系统的应用者提供便利。     

使用Dirichlet自由变形算法实现三维人脸及其变形

人脸是人类相互交流的重要渠道，是人类的喜、怒、哀、乐等复杂表情和语言的载体。因此、具有真实感的三维人脆模型的构造和变形是计算机图形学领域中一个研究热点。如何在三维人脸模型上产生具有真实感的人脸表情和动作．是其中的一个难点。文中介绍了一种基于Delaunay和nfichlev／Voronoi图的Dinchlet自由变形算法（Diridet Free—Form Dcformations，简称DFFD）解决这一问题。文中详细介绍了DFFD技术，并根据MPEG-4的脸部定义参数，应用DFFD对一般人脸进行变形。同时提出了在进行人脸变形时利用脸部定义参数FDP与脸部动画参数FAP分层次控制的方法，这种两级控制点控制的设置，使三维人脸模型产生光滑变形．由此可将人脸各种表情平滑准确地展现出来。     

基于特征和弹性网格模型的快速人脸建模

为解决特定人脸的建模问题提供了一个简单而行之有效的方法。给定特定人脸的正面侧面照片,以及内嵌具有人脸特征信息的弹性人脸网格模型,采用基于小波分析的方法进行人脸特征识别,基于特定人脸的特征线相对于一般人脸模型上的特征线的位移,根据弹性系数求解所有点的位移变化,适配特定人脸几何。纹理映射之后生成能以任意视线方向观察的高度真实感特定人脸。该方法能在廉价的PC平台上快速而方便地得到实现。     

基于照片和一般人脸模型的人脸构造系统

阐述了一般人脸模型及其数据结构,使用正侧面照片和一般人脸模型建立三维个性人脸模型,基于B样条曲线编辑人脸模型,以及系统基于OpenGL的显示编辑环境等内容。这是一个比较简单实用的由二维照片得到三维人脸图形的方法,并且可以通过修改脸部特征的关键点,来比较快速地得到一个以原来模型为基础的新的人脸模型。该系统可在影视、游戏、教学、医疗等诸多方面广泛应用。     

Manifold‐Based 3D Face Caricature Generation with Individualized Facial Feature Extraction

Caricature is an interesting art to express exaggerated views of different persons and things through drawing. The face caricature is popular and widely used for different applications. To do this, we have to properly extract unique/specialized features of a person's face. A person's facial feature not only depends on his/her natural appearance, but also the associated expression style. Therefore, we would like to extract the neutural facial features and personal expression style for different applicaions. In this paper, we represent the 3D neutral face models in BU–3DFE database by sparse signal decomposition in the training phase. With this decomposition, the sparse training data can be used for robust linear subspace modeling of public faces. For an input 3D face model, we fit the model and decompose the 3D model geometry into a neutral face and the expression deformation separately. The neutral geomertry can be further decomposed into public face and individualized facial feature. We exaggerate the facial features and the expressions by estimating the probability on the corresponding manifold. The public face, the exaggerated facial features and the exaggerated expression are combined to synthesize a 3D caricature for a 3D face model. The proposed algorithm is automatic and can effectively extract the individualized facial features from an input 3D face model to create 3D face caricature.     

Automatic creation of a talking head from a video sequence

In this paper, a real-time system to create a talking head from a video sequence without any user intervention is presented. In the proposed system, a probabilistic approach, to decide whether or not extracted facial features are appropriate for creating a three-dimensional (3-D) face model, is presented. Automatically extracted two-dimensional facial features from a video sequence are fed into the proposed probabilistic framework before a corresponding 3-D face model is built to avoid generating an unnatural or nonrealistic 3-D face model. To extract face shape, we also present a face shape extractor based on an ellipse model controlled by three anchor points, which is accurate and computationally cheap. To create a 3-D face model, a least-square approach is presented to find a coefficient vector that is necessary to adapt a generic 3-D model into the extracted facial features. Experimental results show that the proposed system can efficiently build a 3-D face model from a video sequence without any user intervention for various Internet applications including virtual conference and a virtual story teller that do not require much head movements or high-quality facial animation.     

New algorithm for 3D facial model reconstruction and its application in virtual reality

3D human face model reconstruction is essential to the generation of facial animations that is widely used in the field of virtual reality (VR). The main issues of 3D facial model reconstruction based on images by vision technologies are in twofold: one is to select and match the corresponding features of face from two images with minimal interaction and the other is to generate the realistic-looking human face model. In this paper, a new algorithm for realistic-looking face reconstruction is presented based on stereo vision. Firstly, a pattern is printed and attached to a planar surface for camera calibration, and corners generation and corners matching between two images are performed by integrating modified image pyramid Lucas-Kanade (PLK) algorithm and local adjustment algorithm, and then 3D coordinates of corners are obtained by 3D reconstruction. Individual face model is generated by the deformation of general 3D model and interpolation of the features. Finally, realistic-looking human face model     

Tracking and Synthesizing Facial Motions with Dynamic Contours

Many researchers have studied techniques related to the analysis and synthesis of human heads under motion with face deformations. These techniques can be used for defining low-rate image compression algorithms (model-based image coding), cinema technologies, video-phones, as well as for applications of virtual reality, etc. Such techniques need a real-time performance and a strong integration between the mechanisms of motion estimation and those of rendering and animation of the 3D synthetic head/face. In this paper, a complete and integrated system for tracking and synthesizing facial motions in real-time with low-cost architectures is presented. Facial deformations curves represented as spatiotemporal B-splines are used for tracking in order to model the main facial features. In addition, the system proposed is capable of adapting a generic 3D wire-frame model of a head/face to the face that must be tracked; therefore, the simulations of the face deformations are produced by using a realistic patterned face.     

AU-aware Algorithm for 3D Facial Reconstruction

As a critical task in computer vision and animation, 3D face reconstruction can provide 3D model structures and rich semantic information for multi-modal facial applications. However, monocular 2D facial images lack depth information and the parameters of the predicted facial model are not reliable, which causes poor reconstruction results. We propose to employ facial Action Unit (AU) and key facial points which are highly correlated with model parameters as a bridge to guide the regression of model-related parameters and thus solve the ill-posed monocular face reconstruction. Based on existing face reconstruction datasets, we provide a complete semi-automatic labeling scheme for facial AUs and construct a 300W-LP-AU dataset. Furthermore, a 3D face reconstruction algorithm based on AU awareness is put forward to realize end-to-end multi-tasking learning and reduce the overall training difficulty. Experimental results show that the algorithm can improve the face reconstruction performance, with high fidelity of the rebuilt facial model.     

3-D Head Model Retrieval Using a Single Face View Query

In this paper, a novel 3D head model retrieval approach is proposed, in which only a single 2D face view query is required. The proposed approach will be important for multimedia application areas such as virtual world construction and game design, in which 3D virtual characters with a given set of facial features can be rapidly constructed based on 2D view queries, instead of having to generate each model anew. To achieve this objective, we construct an adaptive mapping through which each 2D view feature vector is associated with its corresponding 3D model feature vector. Given this estimated 3D model feature vector, similarity matching can then be performed in the 3D model feature space. To avoid the explicit specification of the complex relationship between the 2D and 3D feature spaces, a neural network approach is adopted in which the required mapping is implicitly specified through a set of training examples. In addition, for efficient feature representation, principal component analysis (PCA) is adopted to achieve dimensionality reduction for facilitating both the mapping construction and the similarity matching process. Since the linear nature of the original PCA formulation may not be adequate to capture the complex characteristics of 3D models, we also consider the adoption of its nonlinear counterpart, i.e., the so-called kernel PCA approach, in this work. Experimental results show that the proposed approach is capable of successfully retrieving the set of 3D models which are similar in appearance to a given 2D face view.