By Zhen Wen
3D Face Processing: Modeling, research and Synthesis introduces the frontiers of 3D face processing innovations. It reports present 3D face processing ideas, 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 tools for modeling complicated normal facial movement, in addition to face visual appeal adaptations as a result of illumination and refined movement. Then the authors follow 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 laptop interplay and video surveillance. It includes a entire survey on present face processing recommendations, that 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)
A): The synthesized face motion. (b): The reconstructed video frame with synthesized face motion. 26L codec. 2001]. Compared to other 3D non-rigid facial motion tracking approaches using single camera, which are cited in Section 1, the features of our tracking system can be summarized as: (1) the deformation space is learned automatically from data such that it avoids manual crafting but still captures the characteristics of real facial motion; (2) it is real-time so that it can be used in real-time human computer interface and coding applications; (3) To reduce “drifting” caused by error accumulation in long-term tracking, it uses templates in both the initial frame and previous frame when estimating the template-matchingbased optical flow (see [Tao and Huang, 1999]); and (4) it is able to recover from temporary loss of tracking by incorporating a template-matching-based face detection module.
A): The generic model in iFACE. (b): A personalized face model based on the CyberwareTM scanner data. (c): The feature points defined on generic model. , 2002], we used MUs to animate models generated by iFACE. We dealt with the MU fitting problem by constructing a mapping between MUs and the face deformation model of iFACE. This technique allowed a key-frame based face animation system to use MUs. First we 28 3D FACE PROCESSING: MODELING, ANALYSIS AND SYNTHESIS selected a set of training facial shapes with known MUPs.
Each phoneme is modeled as a multidimensional Gaussian with mean and covariance A phoneme sequence constrains the temporal trajectory of facial motion at certain time instances. To generate a smooth trajectory given these constraints, we use NURBS (Nonuniform Rational B-splines) interpolation. The NURBS trajectory is defined as: where is the order of the NURBS, is the basis function, is control point of the NURBS, and is the weight of Usually people use or In our framework, we use The phonemes (key facial configurations) are used as control points, which we assume to have Gaussian distributions (the method can be trivially generalized to Gaussian mixtures).