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

Author(s):  
Haibin Fu ◽  
Shaojun Bian ◽  
Ehtzaz Chaudhry ◽  
Andres Iglesias ◽  
Lihua You ◽  
...  
2018 ◽  
Vol 37 (2) ◽  
pp. 523-550 ◽  
Author(s):  
M. Zollhöfer ◽  
J. Thies ◽  
P. Garrido ◽  
D. Bradley ◽  
T. Beeler ◽  
...  

2019 ◽  
Vol 27 (2) ◽  
Author(s):  
Gemma Rotger ◽  
Francesc Moreno-Noguer ◽  
Felipe Lumbreras ◽  
Antonio Agudo

Author(s):  
Claudio Ferrari ◽  
Stefano Berretti ◽  
Alberto del Bimbo

3D face reconstruction from a single 2D image is a fundamental computer vision problem of extraordinary difficulty that dates back to the 1980s. Briefly, it is the task of recovering the three-dimensional geometry of a human face from a single RGB image. While the problem of automatically estimating the 3D structure of a generic scene from RGB images can be regarded as a general task, the particular morphology and non-rigid nature of human faces make it a challenging problem for which dedicated approaches are still currently studied. This chapter aims at providing an overview of the problem, its evolutions, the current state of the art, and future trends.


Author(s):  
Wanshun Gao ◽  
Xi Zhao ◽  
Jun An ◽  
Jianhua Zou

In this paper, we propose a novel approach for 3D face reconstruction from multi-facial images. Given original pose-variant images, coarse 3D face templates are initialized to reconstruct a refined 3D face mesh in an iteration manner. Then, we warp original facial images to the 2D meshes projected from 3D using Sparse Mesh Affine Warp (SMAW). Finally, we weight the face patches in each view respectively and map the patch with higher weight to a canonical UV space. For facial images with arbitrary pose, their invisible regions are filled with the corresponding UV patches. Poisson editing is applied to blend different patches seamlessly. We evaluate the proposed method on LFW dataset in terms of texture refinement and face recognition. The results demonstrate competitive performance compared to state-of-the-art methods.


Author(s):  
Xingjuan Cai ◽  
Yihao Cao ◽  
Yeqing Ren ◽  
Zhihua Cui ◽  
Wensheng Zhang

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