By Zhen Wen
3D Face Processing: Modeling, research and Synthesis introduces the frontiers of 3D face processing suggestions. It reports present 3D face processing options, together with innovations 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 equipment for modeling complicated ordinary facial movement, in addition to face visual appeal diversifications 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 publication 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 includes a entire survey on current face processing strategies, that may function a reference for college students and researchers. It additionally covers in-depth dialogue on face movement research and synthesis algorithms, to be able to gain extra complicated graduate scholars and researchers.
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Extra info for 3D Face Processing: Modeling, Analysis and Synthesis (The International Series in Video Computing)
In order to handle various lighting conditions, the texture part of the training data should cover broad enough lighting conditions. , 2001, Reveret and Essa, 2001], which can capture the real motion characteristics of facial features better than manually defined models. , 1998, Reveret and Essa, 2001] only deal with lips. The trained 3D model is able to encode the information of real lip deformations. , 2001, Reveret and Essa, 2001] to derive basis of facial deformation model. , 2001b]. 2. Geometric MU-based 3D Face Tracking In the conventional 3D non-rigid face tracking algorithm using FACS-based 3D facial deformation model, the subspace spanned by the Action Units (AUs) is used as the high-level knowledge to guide face tracking.
A): The markers are shown as small white dots. (b) and (c): The mesh is shown in two different viewpoints. 3. Learning Holistic Linear Subspace To make complex facial deformation tractable in computational models, people have usually assumed that any facial deformation can be approximated by a linear combination of some basic deformation. In our framework, we make the same assumption, and try to find optimal bases under this assumption. We call these bases Motion Units (MUs). Using MUs, a facial shape can be represented by where denotes the facial shape without deformation, is the mean facial deformation, is the MU set, and is the MU parameter (MUP) set.
1998] used ANN to learn a function, which maps 2D marker movements to the parameters or a physics-based 3D face deformation model. This helped to automate the construction of physicsbased face muscle model, and to improve the animation produced. Moreover, ANN has been used to learn the correlation between facial deformation and other related signals. , 1999]. Principal Component Analysis (PCA) [Jolliffe, 1986] learns orthogonal components that explain the maximum amount of variance in a given data set.
3D Face Processing: Modeling, Analysis and Synthesis (The International Series in Video Computing) by Zhen Wen