3D Single Image Face Reconstruction Approaches With Deep Neural Networks

Abstract

3D facial reconstruction is an emerging and interesting application in the field of computer graphics and computer vision. It is difficult and challenging to reconstruct the 3D facial model from a single photo because of arbitrary poses, non-uniform illumination, expressions, and occlusions. Detailed 3D facial models are difficult to reconstruct because every algorithm has some limitations related to profile view, fine detail, accuracy, and speed. The major problem is to develop 3D face with texture of large poses, wild faces, large training data, and occluded faces. Mostly algorithms use convolution neural networks and deep learning frameworks to create facial model. 3D face reconstruction algorithms used for application such as 3D printing, 3D VR games and facial recognition. Different issues, problems and their proposed solutions are discussed. Different facial dataset and facial 3DMM used for 3D face reconstructing from a single photo are explained. The recent state of art 3D facial reconstruction and 3D face learning methods developed in 2019 is briefly explained.