真实感人脸的形状与表情空间

提出一种建立三维人脸扫描模型参数空间的算法,其中的模板拟和算法基于能量最小的优化机制,通过非线性优化过程求解模板模型在每个顶点上的位移矢量,使之逼近目标模型.优化目标方程由以下测度组成：距离、平滑度以及人脸特征对应,如特征曲线、边界和特征点对等.使用该算法可用于不同人脸以及不同表情模型之间的对应建立,从而获取一致参数化的人脸形状和表情空间.在文中系统中,三维面部特征曲线通过Canny边检测算法自动获取,这样自动检测获取的特征曲线可用于降低三维形状描述的维数,同时完整的面部几何形状通过径向基函数插值得到.在中性人脸和表情人脸模型上所作的一致参数化为许多应用提供了平台,如形状渐变,纹理迁移和表情迁移.考虑到自动提取的特征曲线和二维线画卡通人脸的相似性,使用迭代优化算法实现二维线画卡通人脸姿态到三维真实人脸模型的迁移.     

图像驱动的三维人脸自动生成与编辑算法

针对图像驱动的三维人脸建模这个计算机图形学中的研究热点问题,提出一种采用三维人脸形变模型的三维人脸自动生成与编辑算法.首先建立三维人脸形变模型,由三维人脸数据库统计学习得到线性混合人脸模型,用一个低维的参数向量来描述一个人脸;然后通过人脸检测、人脸对齐、边缘提取等方法从人脸图像中提取人脸的特征,根据这些特征实现三维人脸形变模型与图像的匹配,重建出与图像对应的三维人脸模型;最后,通过改变参数向量的值实现人脸的编辑.对5个输入人脸照片进行了三维人脸模型重建和编辑并且将重建的人脸模型和真实人脸模型进行了对比,实验结果表明,该算法可实现真实化的人脸重建效果.     

超球流形约束下的3DMM分步全局优化

通过综合运用人脸空间的超球流形约束、基于梯度的启发式全局优化、光照的球面谐波描述以及凸包可见点集的直接消隐方法,提出一种三维可形变模型的图像匹配方法.首先通过形状超球流形约束下的全局优化算法求解摄像机参数和形状参数,然后使用以上参数和凸包点集的直接消隐方法确定物像点对应关系,最后根据物像点对应关系由反射率超球流形约束下的全局优化算法求解光照参数和反射率参数.定量的对比实验结果表明,该方法无需借助分区域拟合、人为估计参数值、层次匹配策略或复杂的特征组合,即可由单幅图像恢复三维可形变模型(3DMM)的全部参数.     

基于弱监督学习的三维人脸形状与纹理重建

三维人脸相较于二维人脸包含了更多特征信息, 可应用于如人脸识别、影视娱乐、医疗美容等更多实际应用场景, 因此三维人脸重建技术一直是计算机视觉领域的研究热点. 由于真实三维人脸数据较难获取, 很多基于深度学习的重建算法首先利用传统重建方法为大量二维人脸图像构建三维标签, 作为训练数据, 这些数据可能并不精准, 从而导致算法的重建精度受到影响. 为此, 本文提出一种基于multi-level损失函数的弱监督学习模型, 结合传统三维人脸形变模型3DMM与深度学习方法, 直接从大量无三维标签的二维人脸图像中学习三维人脸特征信息, 从而实现基于单张二维人脸图像的三维人脸重建算法. 此外, 为解决二维人脸图像中常存在遮挡或大姿态情况而影响人脸纹理重建的问题, 本文使用基于CelebAMask-HQ数据集的人脸解析分割算法对图像进行预处理去除遮挡区域. 实验结果表明, 基于本文方法的三维人脸重建质量与重建精度均实现了一定的提升.     

基于形变模型的多角度三维人脸实时重建

摘 要：采用人脸特征点调整三维形变模型的方法应用于面部三维重建,但模型形变的计 算往往会产生误差,且耗时较长。因此运用人脸二维特征点对通用三维形变模型的拟合方法进 行改进,提出了一种视频流的多角度实时三维人脸重建方法。首先利用带有三层卷积网络的 CLNF 算法识别二维特征点,并跟踪特征点位置;然后由五官特征点位置估计头部姿态,更新 模型的表情系数,其结果再作用于 PCA 形状系数,促使当前三维模型发生形变;最后采用 ISOMAP 算法提取网格纹理信息,进行纹理融合形成特定人脸模型。实验结果表明,该方法在 人脸重建过程中具有更好的实时性能,且精确度有所提高。     

一种自动鲁棒的三维人脸重建方法

三维人脸模型已经广泛应用到视频电话、视频会议、影视制作、电脑游戏、人脸识别等多个领域。目前三维人脸建模一般使用多幅图像，且要求表情中性。本文提出了基于正、侧面任意表情三维人脸重建方法。首先对二维图像中的人脸进行特征提取，然后基于三维人脸统计模型，通过缩放、平移、旋转等方法，及全局和局部匹配，获得特定的三维人脸。基于二维图像中的人脸纹理信息，通过纹理映射，获得完整的三维人脸。通过对大量实际二维人脸图像的三维人脸重建，证实了该方法的有效性和鲁棒性。     

从多张非标定图像重建三维人脸

为了在人脸动画中方便地重建真实感强的三维人脸模型,提出一种从多角度、非标定图像重建三维人脸的方法.首先利用一种基于规则网格变形的模型归一化方法建立人脸形变模型;然后分别在5幅人脸图像上手动标注不超过14个关键点,通过拟合图像上的关键点重建个性化的人脸几何形状;最后从每个角度渲染出一幅形状无关纹理,并将5幅形状无关纹理融合成一幅平滑的纹理图像.重建结果显示,该方法能在较少的手动交互的情况下重建出具有真实感的个性化人脸模型.     

基于单张人脸图像三维形状模型重建

在人脸图像识别优化的研究中,针对由单张人脸图像重建三维模型时对人脸图像姿态存在要求的问题,为了提高识别精度,提出基于单张人脸图像姿态预估计和主成分分析(PCA)的形状模型重建算法.首先由三维姿态估计方法得到人脸姿态,并建立人脸形状模型样本库,然后通过选取的特征点,利用主成分分析进行三维人脸形状模型的重构,最后利用径向基函数(RBF)变换和特征点坐标精确调整三维人脸形状模型,并进行仿真.仿真结果表明,重构的三维人脸形状模型效果良好,提高了精度,对有旋转姿态的人脸图像和特征点定位误差也有很好的鲁棒性.     

基于特征分块的三维人脸重建和识别

针对传统三维人脸重建算法效率低且难以满足实际应用的缺陷,提出一种基于特征分块的三维人脸重建算法,并将此算法应用到三维人脸识别中,实现了基于特征分块的加权三维人脸识别。首先,利用基于平面模板的非均匀重采样法对原始数据进行归一化;其次,采用主动形状模型(ASM)算法对三维人脸和二维人脸图像进行特征定位和特征分块;然后,利用基于分块主元分析(PCA)的稀疏形变模型算法实现每个人脸分块的三维重建;最后,实现了此算法在三维人脸识别中的应用。实验表明,此重建算法具有较高的精度和重建效率,还可以达到全局最优,并且可以提高三维人脸的识别率。     

从视频中恢复三维人脸的实时方法

三维人脸恢复是视觉交互的一个难点问题,提出了一种从视频中实时恢复三维人脸的新方法.该方法利用主动形状模型进行人脸特征点提取和跟踪,确保了三维形状恢复和特征跟踪的有效性和一致性;采用非刚体形状和运动估计方法构建三维形变基,有效地适应人脸形状变化的多样性;采用非线性优化算法估算人脸姿态和三维形变基参数,实现了三维人脸形状和姿态的实时恢复.实验结果表明,该方法不仅能从视频中实时恢复三维人脸模型,而且可有效跟踪人脸各种姿态的变化.     

Face recognition based on fitting a 3D morphable model

This paper presents a method for face recognition across variations in pose, ranging from frontal to profile views, and across a wide range of illuminations, including cast shadows and specular reflections. To account for these variations, the algorithm simulates the process of image formation in 3D space, using computer graphics, and it estimates 3D shape and texture of faces from single images. The estimate is achieved by fitting a statistical, morphable model of 3D faces to images. The model is learned from a set of textured 3D scans of heads. We describe the construction of the morphable model, an algorithm to fit the model to images, and a framework for face identification. In this framework, faces are represented by model parameters for 3D shape and texture. We present results obtained with 4,488 images from the publicly available CMU-PIE database and 1,940 images from the FERET database.     

Face Relighting from a Single Image under Arbitrary Unknown Lighting Conditions

In this paper, we present a new method to modify the appearance of a face image by manipulating the illumination condition, when the face geometry and albedo information is unknown. This problem is particularly difficult when there is only a single image of the subject available. Recent research demonstrates that the set of images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated accurately by a low-dimensional linear subspace using a spherical harmonic representation. Moreover, morphable models are statistical ensembles of facial properties such as shape and texture. In this paper, we integrate spherical harmonics into the morphable model framework by proposing a 3D spherical harmonic basis morphable model (SHBMM). The proposed method can represent a face under arbitrary unknown lighting and pose simply by three low-dimensional vectors, i.e., shape parameters, spherical harmonic basis parameters, and illumination coefficients, which are called the SHBMM parameters. However, when the image was taken under an extreme lighting condition, the approximation error can be large, thus making it difficult to recover albedo information. In order to address this problem, we propose a subregion-based framework that uses a Markov random field to model the statistical distribution and spatial coherence of face texture, which makes our approach not only robust to extreme lighting conditions, but also insensitive to partial occlusions. The performance of our framework is demonstrated through various experimental results, including the improved rates for face recognition under extreme lighting conditions.     

Real-time 3D face tracking based on active appearance model constrained by depth data

Active Appearance Model (AAM) is an algorithm for fitting a generative model of object shape and appearance to an input image. AAM allows accurate, real-time tracking of human faces in 2D and can be extended to track faces in 3D by constraining its fitting with a linear 3D morphable model. Unfortunately, this AAM-based 3D tracking does not provide adequate accuracy and robustness, as we show in this paper. We introduce a new constraint into AAM fitting that uses depth data from a commodity RGBD camera (Kinect). This addition significantly reduces 3D tracking errors. We also describe how to initialize the 3D morphable face model used in our tracking algorithm by computing its face shape parameters of the user from a batch of tracked frames. The described face tracking algorithm is used in Microsoft's Kinect system.     

Reconstructing 3D Shape,Albedo and Illumination from a Single Face Image

The morphable model has been employed to efficiently describe 3D face shape and the associated albedo with a reduced set of basis vectors. The spherical harmonics (SH) model provides a compact basis to well approximate the image appearance of a Lambertian object under different illumination conditions. Recently, the SH and morphable models have been integrated for 3D face shape reconstruction. However, the reconstructed 3D shape is either inconsistent with the SH bases or obtained just from landmarks only. In this work, we propose a geometrically consistent algorithm to reconstruct the 3D face shape and the associated albedo from a single face image iteratively by combining the morphable model and the SH model. The reconstructed 3D face geometry can uniquely determine the SH bases, therefore the optimal 3D face model can be obtained by minimizing the error between the input face image and a linear combination of the associated SH bases. In this way, we are able to preserve the consistency between the 3D geometry and the SH model, thus refining the 3D shape reconstruction recursively. Furthermore, we present a novel approach to recover the illumination condition from the estimated weighting vector for the SH bases in a constrained optimization formulation independent of the 3D geometry. Experimental results show the effectiveness and accuracy of the proposed face reconstruction and illumination estimation algorithm under different face poses and multiple‐light‐source illumination conditions.     

A lighting robust fitting approach of 3D morphable model for face reconstruction

Three-dimensional morphable model (3DMM) is a powerful tool for recovering 3D shape and texture from a single facial image. The success of 3DMM relies on two things: an effective optimization strategy and a realistic approach to synthesizing face images. However, most previous methods have focused on developing an optimization strategy under Phong’s synthesis approach. In this paper, we adopt a more realistic synthesis technique that fully considers illumination and reflectance in the 3DMM fitting process. Using the sphere harmonic illumination model (SHIM), our new synthesis approach can account for more lighting factors than Phong’s model. Spatially varying specular reflectance is also introduced into the synthesis process. Under SHIM, the cost function is nearly linear for all parameters, which simplifies the optimization. We apply our new optimization algorithm to determine the shape and texture parameters simultaneously. The accuracy of the recovered shape and texture can be improved significantly by considering the spatially varying specular reflectance. Hence, our algorithm produces an enhanced shape and texture compared with previous SHIM-based methods that recover shape from feature points. Although we use just a single input image in a profile pose, our approach gives plausible results. Experiments on a well-known image database show that, compared to state-of-the-art methods based on Phong’s model, the proposed approach enhances the robustness of the 3DMM fitting results under extreme lighting and profile pose.     

结合特征适配与拉普拉斯形变的3维人脸重建

目的 数字娱乐产业的发展要求3维人脸重建技术能重建高分辨率3维人脸,并具有较高计算效率和重建准确性。针对这一情况,提出一种基于单幅图像的高分辨率3维人脸重建方法。方法 该方法包含特征适配与拉普拉斯形变两部分。预先用1组3维人脸样本上的3维特征构造可变形模型。给定图像时,从其上自动提取2维特征点,并根据获得问题最优解的必要条件进行特征适配以重建个性化3维特征;然后基于拉普拉斯方法,用该3维特征对一般人脸模型进行变形以获得特定高分辨率3维人脸;最后通过纹理合成获得真实感人脸。结果 用本文方法和已有方法分别进行可变形模型适配和模型变形,本文的特征适配方法具有更快的收敛速度和更高的准确性,拉普拉斯方法具有更小的重建误差。纹理映射后的3维人脸具有很好的视觉效果。结论 本文方法将特征适配与拉普拉斯形变结合起来进行高分辨率3维人脸重建。实验结果表明所提出的方法具有较高的计算效率和准确性,能实现较为理想的高分辨率3维人脸重建。     

基于压缩感知理论的3维人脸快速重建

形变模型是当前人脸重建研究中的一种主要方法。针对形变模型方法中模型构建的缺陷,提出一种基于压缩感知理论的快速三维人脸重建方法。首先,利用压缩感知理论估计三维原型人脸与目标人脸的形状相似性,根据相似性对原型样本进行筛选并构建相应的形变模型,提高建模精度和效率;然后,利用特征点信息进行稀疏模型匹配,并结合径向基函数插值重建生成特定的三维人脸,提高重建表面的平滑性。在BJUT三维数据库和CAS_PEAL二维数据库上的实验结果表明,与经典方法相比,本文方法能够有效地提高重建精度和速度,重建人脸具有较强真实感。     

2D face fitting-assisted 3D face reconstruction for pose-robust face recognition

Recent face recognition algorithm can achieve high accuracy when the tested face samples are frontal. However, when the face pose changes largely, the performance of existing methods drop drastically. Efforts on pose-robust face recognition are highly desirable, especially when each face class has only one frontal training sample. In this study, we propose a 2D face fitting-assisted 3D face reconstruction algorithm that aims at recognizing faces of different poses when each face class has only one frontal training sample. For each frontal training sample, a 3D face is reconstructed by optimizing the parameters of 3D morphable model (3DMM). By rotating the reconstructed 3D face to different views, pose virtual face images are generated to enlarge the training set of face recognition. Different from the conventional 3D face reconstruction methods, the proposed algorithm utilizes automatic 2D face fitting to assist 3D face reconstruction. We automatically locate 88 sparse points of the frontal face by 2D face-fitting algorithm. Such 2D face-fitting algorithm is so-called Random Forest Embedded Active Shape Model, which embeds random forest learning into the framework of Active Shape Model. Results of 2D face fitting are added to the 3D face reconstruction objective function as shape constraints. The optimization objective energy function takes not only image intensity, but also 2D fitting results into account. Shape and texture parameters of 3DMM are thus estimated by fitting the 3DMM to the 2D frontal face sample, which is a non-linear optimization problem. We experiment the proposed method on the publicly available CMUPIE database, which includes faces viewed from 11 different poses, and the results show that the proposed method is effective and the face recognition results toward pose variants are promising.