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 incorporates a accomplished survey on present face processing ideas, that could function a reference for college kids and researchers. It additionally covers in-depth dialogue on face movement research and synthesis algorithms, with a purpose to gain extra complicated graduate scholars and researchers.
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Additional resources for 3D Face Processing (The Kluwer International Series in Video Computing): Modeling, Analysis, and Synthesis
The top row is the frontal view and the bottom row is the side view. 6. based MU. (a): The neutral face side view. (b): The face deformed by one right cheek parts- than 100 holistic MUs are need to achieve a 90% reconstruction accuracy. That means, although some local deformation is induced by only one parts-based MU, more than 100 holistic MUs may be needed in order to achieve good analysis and synthesis quality. 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.
For example, if MU statistics over a large set of different face geometries are available, one can systematically derive the geometry-to-MU mapping using machine-learning techniques. On the other hand, If multiple MU sets are available, which sample different positions of the same face, it is possible to combine them to increase the spatial resolution of MU because markers in MU are usually sparser than face geometry mesh. The first step adjusts MUs to a face model with different geometry. The fundamental problem is to find a mapping from face geometry to MUs.
Massaro et al. , 1999] trained multilayer perceptrons (MLP) to map LPC cepstral parameters to face animation parameters. They try to model the coarticulation by considering the speech context information of five backward and five forward time windows. Another way to model speech context information is to use time delay neural networks (TDNNs) model to perform temporal processing. Lavagetto [Lavagetto, 1995] and Curinga et al. , 1996] trained TDNN to map LPC cepstral coefficients of speech signal to lip animation parameters.