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.
3D face and 3D hair reconstruction are interesting and emerging applications within the fields of computer vision, computer graphics, and cyber-physical systems. It is a difficult and challenging task to reconstruct the 3D facial model and 3D facial hair from a single photo due to arbitrary poses, facial beard, non-uniform illumination, expressions, and occlusions. Detailed 3D facial models are difficult to reconstruct because every algorithm has some limitations related to profile view, beard face, fine detail, accuracy, and robustness. The major problem is to develop 3D face with texture of large, beard, and wild poses. Mostly algorithms use convolution neural networks and deep learning frameworks to develop 3D face and 3D hair. The latest and state-of-the-art 3D facial reconstruction and 3D face hair approaches are described. Different issues, problems regarding 3D facial reconstruction, and their proposed solutions have been discussed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.