By Zhen Wen
3D Face Processing: Modeling, research and Synthesis introduces the frontiers of 3D face processing recommendations. It experiences present 3D face processing options, together with concepts 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 advanced typical 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 popularity 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 finished survey on present face processing concepts, which may function a reference for college kids and researchers. It additionally covers in-depth dialogue on face movement research and synthesis algorithms, on the way to profit extra complicated graduate scholars and researchers.
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Extra resources for 3D Face Processing: Modeling, Analysis and Synthesis (The International Series in Video Computing)
Efficient and effective facial motion analysis and synthesis requires a compact yet powerful model to capture real facial motion characteristics. For this purpose, analysis of real facial motion data is needed because of the high complexity of human facial motion. We first give a review of previous works on 3D face motion models in Section 1. Then, in Section 2, we introduce the motion capture database used in our framework. Section 3 and 4 present our methods for learning holistic and parts-based spatial geometric facial motion models, respectively.
G. ). The extracted low-level image features are compared with templates to estimate the shapes of facial features. However, it is not robust enough to use low-level image features alone. The error will be accumulated with the increase in number of frames being tracked. High-level knowledge of facial deformation must be used to handle error accumulation problem by imposing constraints on the possible deformed facial shapes. It has been shown that robust tracking algorithm needs to integrate low-level image information and high-level knowledge.
The estimations are then nonlinearly weighted to produce the final visual estimation. The Gaussian mixture approach produces smoother results than the vector quantization approach. However, neither of these two approach described consider phonetic context information, which is very important for modeling mouth coarticulation during speech. Neural network based approaches try to find nonlinear audio-to-visual mappings. Morishima and Harashima [Morishima and Harashima, 1991] trained a three 44 3D FACE PROCESSING: MODELING, ANALYSIS AND SYNTHESIS layer neural network to map LPC Cepstrum speech coefficients of one time step speech signals to mouth-shape parameters for five vowels.
3D Face Processing: Modeling, Analysis and Synthesis (The International Series in Video Computing) by Zhen Wen