State-of-the-Art in 3D Face Reconstruction from a Single RGB Image

Authors: Fu, H., Bian, S., Chaudhry, E., Iglesias, A., You, L. and Zhang, J.J.

Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume: 12746 LNCS

Pages: 31-44

eISSN: 1611-3349

ISBN: 9783030779764

ISSN: 0302-9743

DOI: 10.1007/978-3-030-77977-1_3

Abstract:

Since diverse and complex emotions need to be expressed by different facial deformation and appearances, facial animation has become a serious and on-going challenge for computer animation industry. Face reconstruction techniques based on 3D morphable face model and deep learning provide one effective solution to reuse existing databases and create believable animation of new characters from images or videos in seconds, which greatly reduce heavy manual operations and a lot of time. In this paper, we review the databases and state-of-the-art methods of 3D face reconstruction from a single RGB image. First, we classify 3D reconstruction methods into three categories and review each of them. These three categories are: Shape-from-Shading (SFS), 3D Morphable Face Model (3DMM), and Deep Learning (DL) based 3D face reconstruction. Next, we introduce existing 2D and 3D facial databases. After that, we review 10 methods of deep learning-based 3D face reconstruction and evaluate four representative ones among them. Finally, we draw conclusions of this paper and discuss future research directions.

https://eprints.bournemouth.ac.uk/35872/

Source: Scopus