Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: For realistic speech animation, smooth viseme and expression transitions, blending and co-articulation have been so far studied and experimented widely. ,  describe an approach for speech animation by smooth viseme transition. Though this method cannot form an alternative to the coarticulation phenomenon, it certainly takes us a step nearer to realistic speech animation. The approach is devised as a result of the Principal Component Analysis of facial capture data extracted using an optical tracking system. The system extracts the 3D positions of markers attached at the specific feature point locations on face to capture the facial movements of a talking person.  form a vector space representation by using the Principal Component Analysis of this data.  call this space the “viseme space”.  use the viseme space to generate convincing speech animation and to make smooth transitions from one viseme to another. As the analysis and the resulting viseme space automatically consider the dynamics of and the deformation constraints on the facial movements, the resulting facial animation is very realistic.
['Sumedha Kshirsagar', 'Nadia Magnenat-Thalmann']
I am writing a research paper on a idea Viseme space for realistic speech animation.
[{"from": "human", "value": "I am writing a research paper on a idea Viseme space for realistic speech animation."}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: For realistic speech animation, smooth viseme and expression transitions, blending and co-articulation have been so far studied and experimented widely. ,  describe an approach for speech animation by smooth viseme transition. Though this method cannot form an alternative to the coarticulation phenomenon, it certainly takes us a step nearer to realistic speech animation. The approach is devised as a result of the Principal Component Analysis of facial capture data extracted using an optical tracking system. The system extracts the 3D positions of markers attached at the specific feature point locations on face to capture the facial movements of a talking person.  form a vector space representation by using the Principal Component Analysis of this data.  call this space the \u201cviseme space\u201d.  use the viseme space to generate convincing speech animation and to make smooth transitions from one viseme to another. As the analysis and the resulting viseme space automatically consider the dynamics of and the deformation constraints on the facial movements, the resulting facial animation is very realistic."}]