Abstract: A method for generating three-dimensional facial models and photorealistic textures from inferences using deep neural networks relies upon generating a low frequency and a high frequency albedo map of the full and partial face, respectively. Then, the high frequency albedo map may be used for comparison with correlation matrices generated by a neural network trained by a large scale, high-resolution facial dataset with simulated partial visibility. The corresponding correlation matrices of the complete facial textures can then be retrieved. Finally, a full facial texture map may be synthesized, using convex combinations of the correlation matrices. A photorealistic facial texture for the three-dimensional face rendering can be obtained through optimization using the deep neural network and a loss function that incorporates the blended target correlation matrices.

Abstract: A method for generating three-dimensional facial models and photorealistic textures from inferences using deep neural networks relies upon generating a low frequency and a high frequency albedo map of the full and partial face, respectively. Then, the high frequency albedo map may be used for comparison with correlation matrices generated by a neural network trained by a large scale, high-resolution facial dataset with simulated partial visibility. The corresponding correlation matrices of the complete facial textures can then be retrieved. Finally, a full facial texture map may be synthesized, using convex combinations of the correlation matrices. A photorealistic facial texture for the three-dimensional face rendering can be obtained through optimization using the deep neural network and a loss function that incorporates the blended target correlation matrices.