Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Linear models, particularly those based on principal component analysis (PCA), have been used successfully on a broad range of human face-related applications. Although PCA models achieve high compression, they have not been widely used for animation in a production environment because their bases lack a semantic interpretation. Their parameters are not an intuitive set for animators to work with.   present a linear face modelling approach that generalises to unseen data better than the traditional holistic approach while also allowing  click-and-drag  interaction for animation. Our model is composed of a collection of PCA sub-models that are independently trained but share boundaries. Boundary consistency and user-given constraints are enforced in a soft least mean squares sense to give flexibility to the model while maintaining coherence. Our results show that the region-based model generalises better than its holistic counterpart when describing previously unseen motion capture data from multiple subjects. The decomposition of the face into several regions, which  determine automatically from training data, gives the user localised manipulation control. This feature allows to use the model for face posing and animation in an intuitive style.
['J. Rafael Tena', 'Fernando De la Torre', 'Iain A. Matthews']
I am writing a research paper on a idea Interactive region-based linear 3D face models
[{"from": "human", "value": "I am writing a research paper on a idea Interactive region-based linear 3D face models"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Linear models, particularly those based on principal component analysis (PCA), have been used successfully on a broad range of human face-related applications. Although PCA models achieve high compression, they have not been widely used for animation in a production environment because their bases lack a semantic interpretation. Their parameters are not an intuitive set for animators to work with.   present a linear face modelling approach that generalises to unseen data better than the traditional holistic approach while also allowing  click-and-drag  interaction for animation. Our model is composed of a collection of PCA sub-models that are independently trained but share boundaries. Boundary consistency and user-given constraints are enforced in a soft least mean squares sense to give flexibility to the model while maintaining coherence. Our results show that the region-based model generalises better than its holistic counterpart when describing previously unseen motion capture data from multiple subjects. The decomposition of the face into several regions, which  determine automatically from training data, gives the user localised manipulation control. This feature allows to use the model for face posing and animation in an intuitive style."}]