Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose the efficient and accurate hierarchical ICIA fitting method for 3D Morphable Models (3DMMs). The conventional ICIA fitting method for 3DMMs requires a long computation time because the 3D face model contains a large number of vertices and it also requires to compute the Hessian matrix using the visible vertices every iteration. For the efficient fitting,  use the hierarchical fitting that use a set of multi-resolution 3D face model and the Gaussian image pyramid. For more accurate fitting,  use a two-stage parameter update that only update the rigid and the texture parameters and then update all parameters after the initial convergence.  present several experiment results to prove that our proposed method shows better performance than previous works.
['Bong-Nam Kang', 'Daijin Kim', 'Hyeran Byun']
I am writing a research paper on a idea An efficient and accurate hierarchical ICIA fitting method for 3D Morphable Models
[{"from": "human", "value": "I am writing a research paper on a idea An efficient and accurate hierarchical ICIA fitting method for 3D Morphable Models"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose the efficient and accurate hierarchical ICIA fitting method for 3D Morphable Models (3DMMs). The conventional ICIA fitting method for 3DMMs requires a long computation time because the 3D face model contains a large number of vertices and it also requires to compute the Hessian matrix using the visible vertices every iteration. For the efficient fitting,  use the hierarchical fitting that use a set of multi-resolution 3D face model and the Gaussian image pyramid. For more accurate fitting,  use a two-stage parameter update that only update the rigid and the texture parameters and then update all parameters after the initial convergence.  present several experiment results to prove that our proposed method shows better performance than previous works."}]