By Zhen Wen
3D Face Processing: Modeling, research and Synthesis introduces the frontiers of 3D face processing ideas. It stories latest 3D face processing innovations, together with ideas for 3D face geometry modeling; 3D face movement modeling; and 3D face movement monitoring and animation. Then it discusses a unified framework for face modeling, research and synthesis. during this framework, the authors current new tools for modeling advanced typical facial movement, in addition to face visual appeal adaptations as a result of illumination and refined movement. Then the authors practice the framework to stand monitoring, expression attractiveness and face avatar for HCI interface. They finish this ebook with reviews on destiny paintings within the 3D face processing framework. 3D Face Processing: Modeling, research and Synthesis will curiosity these operating in face processing for clever human laptop interplay and video surveillance. It incorporates a accomplished survey on latest face processing innovations, that may function a reference for college students and researchers. It additionally covers in-depth dialogue on face movement research and synthesis algorithms, so as to gain extra complex graduate scholars and researchers.
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Extra resources for 3D Face Processing: Modeling, Analysis and Synthesis (The International Series in Video Computing)
Compared to other 3D non-rigid facial motion tracking approaches using single camera, which are cited in Section 1, the features of our tracking system can be summarized as: (1) the deformation space is learned automatically from data such that it avoids manual crafting but still captures the characteristics of real facial motion; (2) it is real-time so that it can be used in real-time human computer interface and coding applications; (3) To reduce “drifting” caused by error accumulation in long-term tracking, it uses templates in both the initial frame and previous frame when estimating the template-matchingbased optical flow (see [Tao and Huang, 1999]); and (4) it is able to recover from temporary loss of tracking by incorporating a template-matching-based face detection module.
We also utilize the statistics of the training data in a way that covariance of the key shapes are used as weights in NURBS. This helps to increase the likelihood of the generated trajectory. The details about temporal trajectory modeling is discussed in Chapter 5. In our framework, we can alternatively infer facial derormation dynamics from correlated signals, such as in speech-driven animation and visual face tracking. In that case, the facial deformation is inferred from input signals at every time instance.
1993] uses a parametric geometrical face model, called Candide. The Candide model contains a set of parameters for controlling facial shape. Tao and Huang [Tao and Huang, 1999] use a piecewise Bezier volume deformable face model, which can be deformed by changing the coordinates of the control vertices of the Bezier volumes. In [Essa and Pentland, 1997], Essa and Pentland extended a mesh face model, which was developed by Platt and Badler [Platt and Badler, 1981], into a topologically invariant physics-based model by adding anatomy-based “muscles,” which is defined by FACS.