3D Face Processing (The Kluwer International Series in Video by Ming-Hsuan Yang, Narendra Ahuja

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 features a entire survey on current face processing options, that may function a reference for college kids and researchers. It additionally covers in-depth dialogue on face movement research and synthesis algorithms, to be able to profit extra complicated graduate scholars and researchers.

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

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First, the text stream is fed into the TTS engine. TTS parses the text and generates the corresponding phoneme sequence, the timing information of phonemes, and the synthesized speech stream. Each phoneme is mapped to a viseme based on a lookup table. Each viseme is a key frame. Therefore, the text is translated in to a key frame sequence. A temporal trajectory is then synthesized based on the key frame sequence using the technique described in Section 2. In the framework, we use a label system that has forty-four phonemes.

It is well known that color segmentation is sensitive to lighting conditions and the effectiveness of color segmentation depends on the subject. This can be partially solved by training a color classifier for each individual. Nevertheless, these two approaches do not handle 3D rotation, translation and appearance changes of lips. 2 Snake model Kass et al. , 1988] propose the snake for tracking deformable contours. It starts from an initial starting point and deforms itself to match with the nearest salient contour.

1997] trains hidden Markov models (HMMs) [Rabiner, 1989] to automatically label phonemes in both training audio track and new audio track. It models shortterm mouth co-articulation using triphones. The mouth images for a new audio track are generated by reordering the mouth images in the training footage, which requires a very large database. Video Rewrite is an offline approach and needs large computation resources. Chen and Rao [Chen and Rao, 1998] train HMMs to segment the audio feature vectors of isolated words into state sequences.

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