By Zhen Wen
3D Face Processing: Modeling, research and Synthesis introduces the frontiers of 3D face processing ideas. It studies current 3D face processing strategies, together with thoughts 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 normal facial movement, in addition to face visual appeal diversifications as a result of illumination and sophisticated movement. Then the authors practice the framework to stand monitoring, expression popularity and face avatar for HCI interface. They finish this e-book 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 machine interplay and video surveillance. It features a entire survey on present face processing strategies, that can function a reference for college kids and researchers. It additionally covers in-depth dialogue on face movement research and synthesis algorithms, so that it will profit extra complex graduate scholars and researchers.
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Additional resources for 3D Face Processing: Modeling, Analysis and Synthesis (The International Series in Video Computing)
17 Future Research Direction of 3D Face Modeling In the future, one promising research direction is to improve face modeling tools which use one face image, along the line of the “linear class geometries” work [Blanz and Vetter, 1999]. Improvements in the following aspects are highly desirable. 3D face databases: The expressiveness of the “linear class geometries” approach is decided by the 3D face model databases. In order to generate convincing 3D face models for more people other than young Caucasian in the Blanz and Vetter’99 database [Blanz and Vetter, 1999], more 3D face scan data are needed to be collected for people of different races and different ages.
In a key-frame based animation system, these training shapes can be represented by linear combinations of key frames. , 2002]. This method enabled us to use MUs for animation in a traditional key-frame-based animation system, such as iFACE. However, key frames of a certain system may not be expressive enough to take advantage of the motion details in MUs. Thus, the facial deformation information can be lost during conversion between parameters of key-frame combination and MUPs. Alternatively, interpolationbased techniques for re-targeting animation to new models, such as [Noh and Neumann, 2001], could be used for MU fitting.
In Section 2, we will describe our face modeling tool for our face processing framework. After that, some future directions will be discussed in Section 3. 2. Face Modeling Tools in iFACE We have developed iFACE system which provides functionalities for face modeling and face animation. It provides a research platform for the 3D face processing framework. The iFACE system takes the CyberwareTMscanner data of a subject’s head as input and allows the user to interactively fit a generic face model to the CyberwareTM scanner data.