By Zhen Wen
3D Face Processing: Modeling, research and Synthesis introduces the frontiers of 3D face processing suggestions. It stories present 3D face processing strategies, together with options 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 practice 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 machine interplay and video surveillance. It encompasses a accomplished survey on current face processing strategies, which could function a reference for college students and researchers. It additionally covers in-depth dialogue on face movement research and synthesis algorithms, that allows you to gain extra complex graduate scholars and researchers.
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Additional resources for 3D Face Processing: Modeling, Analysis and Synthesis (The International Series in Video Computing)
The Gaussian mixture approach [Rao and Chen, 1996] models the joint probability distribution of the audio-visual vectors as a Gaussian mixture. Each Gaussian mixture component generates an optimal estimation for a visual feature given an audio feature. 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.
They are defined interactively by artists. 3 Summary Among the many approaches for 3D face modeling, 3D range scanners provide high quality 3D measurements for building realistic face models. However, most scanners are still very expensive and need to be used in controlled environments. In contrast, image-based approaches have low cost and can be used in 14 3D FACE PROCESSING: MODELING, ANALYSIS AND SYNTHESIS more general conditions. But the 3D measurements in image-based approaches are much noisier which could degrade the quality of the reconstructed 3D model.
The 3D models are generated by cleaning up, registering the CyberwareTM scan data. Given a new face image, a fitting algorithm is used to estimate the coefficients of the linear combination. They have demonstrated that linear classes of face geometries and images are very powerful in generating convincing 3D human face models from images. For this approach to achieve convincing results, it requires that the novel is similar to faces in the database and the feature points of the initial 3D model is roughly aligned with the input face image.
3D Face Processing: Modeling, Analysis and Synthesis (The International Series in Video Computing) by Zhen Wen