Download 3D Face Processing: Modeling, Analysis and Synthesis (The by Zhen Wen PDF

By Zhen Wen

3D Face Processing: Modeling, research and Synthesis introduces the frontiers of 3D face processing strategies. It reports current 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 ordinary facial movement, in addition to face visual appeal adaptations because of illumination and refined movement. Then the authors follow the framework to stand monitoring, expression reputation and face avatar for HCI interface. They finish this ebook 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 desktop interplay and video surveillance. It encompasses a complete survey on latest face processing innovations, that could function a reference for college kids and researchers. It additionally covers in-depth dialogue on face movement research and synthesis algorithms, in an effort to gain extra complicated graduate scholars and researchers.

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Additional resources for 3D Face Processing: Modeling, Analysis and Synthesis (The International Series in Video Computing)

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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.

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. Similar to MU, AUs are defined such that arbitrary facial deformation is approximated by a linear combination of AUs. However, the AUs are usually manually designed. For these approaches, our automatically learned MUs can be used in place of the manually designed AUs.

The advantage of this approach is that it employs only a few key facial shapes estimated from training data. In order to get a smooth trajectory, we use NURBS (Nonuniform Rational B-splines) interpolation, which is easy to 30 3D FACE PROCESSING: MODELING, ANALYSIS AND SYNTHESIS learn and perform well in general conditions. We also utilize the statistics of the training data in a way that covariance of the key shapes are used as weights in NURBS. This helps to increase the likelihood of the generated trajectory.

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