2017
DOI: 10.1007/s11042-017-4437-z
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Lip syncing method for realistic expressive 3D face model

Abstract: Lip synchronization of 3D face model is now being used in a multitude of important fields. It brings a more human, social and dramatic reality to computer games, films and interactive multimedia, and is growing in use and importance. High level of realism can be used in demanding applications such as computer games and cinema. Authoring lip syncing with complex and subtle expressions is still difficult and fraught with problems in terms of realism. This research proposed a lip syncing method of realistic expre… Show more

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Cited by 12 publications
(8 citation statements)
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References 36 publications
(59 reference statements)
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“…Learning in Neural Networks has made it possible for scientists and researchers to create various applications for multiple industries and create ease in everyday life. The methods reviewed in this paper, namely, Artificial Neural Networks, Backpropagation, and Particle Swarm Optimization have a significant role in Neural Networks in understanding real world problems and tasks, such as image processing, speech or character recognition, and intrusion detection [20,21] Contributions of these methods are significant; however, each category still lacks the definite success as these fields are still progressing and improving on enclosing the gap which exists between its theory and practice. The novelty of the classification concept in Artificial Neural Networks, in particular Particle Swarm Optimization and Backpropagation, means that this field of research is openly, actively, and highly researched every year.…”
Section: Resultsmentioning
confidence: 99%
“…Learning in Neural Networks has made it possible for scientists and researchers to create various applications for multiple industries and create ease in everyday life. The methods reviewed in this paper, namely, Artificial Neural Networks, Backpropagation, and Particle Swarm Optimization have a significant role in Neural Networks in understanding real world problems and tasks, such as image processing, speech or character recognition, and intrusion detection [20,21] Contributions of these methods are significant; however, each category still lacks the definite success as these fields are still progressing and improving on enclosing the gap which exists between its theory and practice. The novelty of the classification concept in Artificial Neural Networks, in particular Particle Swarm Optimization and Backpropagation, means that this field of research is openly, actively, and highly researched every year.…”
Section: Resultsmentioning
confidence: 99%
“…In this work (8) the physical based simulation is approached from the perspective of low-dimensional linear subspaces like the principal component analysis, which represent the basis vector of the current pose of body and state of cloth, termed the temporal adaptive bases. For creating the low-dimensional bases of the pose space, (5) employed insight from a simulation train data to learn the pose space. However, this approach of learning from simulated data limits the actual dynamics of body pose and cloth state in cloth simulation.…”
Section: Previous Workmentioning
confidence: 99%
“…The raised cosine function (RCF) (13) is adopted for determining the list of constraints + and estimation of positions { , … , }as in (5). By using the RCF, the estimate positions can be modified in such a way that all the constraints of a given cloth fabric, both in a static and dynamic position, is satisfied.…”
Section: Raised Cosine Function (Rcf)mentioning
confidence: 99%
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