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

Fu, H., Bian, S., Chaudhry, E., Iglesias, A., You, L. and Zhang, J. J., 2021. State-of-the-Art in 3D Face Reconstruction from a Single RGB Image. In: Computational Science - ICCS 2021 Conference Proceedings (Lecture Notes in Computer Science 12746), 16-18 June 2021, Krakow, Poland, 31 - 44.

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State-of-the-art in 3D face reconstruction from a single RGB image -18032021 - Copy.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


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


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.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Monocular RGB Image; 3D Face Reconstruction; 3D Morphable Model; Shape-from-shading; Deep Learning; 3D Face Database
Group:Faculty of Media & Communication
ID Code:35872
Deposited By: Symplectic RT2
Deposited On:05 Aug 2021 10:58
Last Modified:14 Mar 2022 14:28


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