M-Face: An Appearance-Based Photorealistic Model for Multiple Facial Attributes Rendering
M-Face: An Appearance-Based Photorealistic Model for Multiple Facial Attributes Rendering
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DOI：
10.1109/tcsvt.2006.877398
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发表时间：
2006-07
影响因子：
8.4
通讯作者：
Yun Fu;Nanning Zheng
中科院分区：
文献类型：
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作者：
Yun Fu;Nanning Zheng
提出了一种新的基于外观的真实感人脸建模框架，称为合并脸（M-Face），并应用于生成旋转视图中的情感人脸属性。假设人脸属于线性对象类和朗伯对象类，我们跨越人脸空间和属性空间，分别使用组的原型和合并比率图像（MRI）。MRI被定义为个体表达比率图像、老化比率图像和照明比率图像（商图像）的无缝混合。M-Face集成了视图空间投影、形状漫画和纹理MRI映射技术。从平均脸，漫画形状重塑更明显的夸张个人的独特性，而重新渲染的纹理倍增的MRI信息在漫画。基于M-Face模型，表情变形，时序老化或再生，和照明变化可以无缝地合并在一个真实感风格上所需的视图旋转的脸产生的视图变形。该框架具有以下优点。首先，在不削弱真实感效果的情况下避免了3D重建。其次，实验证明，形状漫画和纹理MRI映射的集成是一个有效的和计算成本低廉的真实感人脸合成策略。最后，M-Face是一个2-D参数驱动模型，它高度简化了用户操作。M-Face在虚拟人脸、语音驱动的说话人头像、数字绘画、电影制作、低比特率通信等领域有着广泛的应用前景
A novel framework for appearance-based photorealistic facial modeling, called Merging Face (M-Face), is presented and applied to generate emotional facial attributes in rotated views. Assuming that human faces belong to both the linear object class and the Lambertian object class, we span the face space and attribute space, respectively, by using groups of prototypes and merging ratio image (MRI). The MRI is defined as the seamless blend of individual expression ratio image, aging ratio image, and illumination ratio image (quotient image). The M-Face integrates the view space projection, shape caricaturing, and texture MRI-mapping techniques. Derived from the average face, the caricatured shape is reshaped to be more distinct by exaggerating individual distinctiveness, while the rerendered texture multiplies the MRI information during the caricaturing. Based on the M-Face model, the expression morphing, chronological aging or rejuvenating, and illumination variance can be merged seamlessly in a photorealistic style on desired view-rotated faces yielded by view morphing. This framework has the following advantages. First, 3-D reconstruction is avoided without weakening photorealistic effects. Second, the integration of shape caricaturing and texture MRI-mapping proves by experiments to be an efficient and computational inexpensive strategy for realistic face synthesis. Finally, the M-Face is a 2-D parameter-driven model, which highly simplifies user manipulations. The potential applications of M-Face are in various fields like virtual human face, speech-driven talking head, digital painting, film-making, and low-bit-rate communication