3D Face Processing (The Kluwer International Series in Video by Ming-Hsuan Yang, Narendra Ahuja

By Ming-Hsuan Yang, Narendra Ahuja

3D Face Processing: Modeling, research and Synthesis will curiosity these operating in face processing for clever human laptop interplay and video surveillance. It includes a accomplished survey on latest face processing thoughts, which may function a reference for college kids and researchers. It additionally covers in-depth dialogue on face movement research and synthesis algorithms, so one can profit extra complex graduate scholars and researchers.

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By Ming-Hsuan Yang, Narendra Ahuja

3D Face Processing: Modeling, research and Synthesis will curiosity these operating in face processing for clever human laptop interplay and video surveillance. It includes a accomplished survey on latest face processing thoughts, which may function a reference for college kids and researchers. It additionally covers in-depth dialogue on face movement research and synthesis algorithms, so one can profit extra complex graduate scholars and researchers.

Show description

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Extra info for 3D Face Processing (The Kluwer International Series in Video Computing): Modeling, Analysis, and Synthesis

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Waters and Levergood [Waters and Levergood, 1993] used sinusoidal interpolation scheme for temporal modeling. Pelachaud et al. , 1991], Cohen and Massaro [Cohen and Massaro, 1993] customized co-articulation functions based on prior knowledge, to model the temporal trajectory between given key shapes. Physics-based methods solve dynamics equations for these trajectories. Recently, statistical methods have been applied in facial temporal deformation modeling. Hidden Markov Models (HMM) trained from motion capture data are shown to be useful to capture the dynamics of natural facial deformation [Brand, 1999].

Therefore, we can have more flexibility in using parts-based MUs. For example, if we are only interested in motion in forehead, Learning Geometric 3D Facial Motion Model 27 we only need to capture data about face with mainly forehead motion, and learn parts-based MUs from the data. In face animation, people often want to animate local region separately. This task can be easily achieved by adjusting MUPs of parts-based MUs separately. g. the lips). Furthermore, tracking using parts-based MUs is more robust because local error will not affect distant regions.

The movements at other places need to be interpolated. We call this process “MU” fitting. In our framework, we use the face models generated by “iFACE” for MUbased face animation. , 2001a]. 7(a). 7(b) shows a personalized model, which we customize based on the CyberwareTM scanner data for that person. 7(c) shows the feature points we define on the iFACE generic model, which we use for MU fitting. 7. (a): The generic model in iFACE. (b): A personalized face model based on the CyberwareTM scanner data.

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