scholarly journals Fast Modelling Algorithm for Realistic Three-Dimensional Human Face for Film and Television Animation

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Limin Xu
Keyword(s):  
Three Dimensional ◽  
Reconstruction Method ◽  
Feature Points ◽  
Human Face ◽  
3D Face Reconstruction ◽  
3D Face ◽  
Film And Television ◽  
Face Reconstruction ◽  
Reconstruction Performance ◽  
The Face

Aiming at the face photos of film and television animation, this paper proposes a new fast three-dimensional (3D) face modelling algorithm. First of all, based on the LBF algorithm, this paper proposes a multifeature selection idea to automatically detect multiple features of the face. Secondly, in order to solve the shortcomings of slow training speed while achieving large pose face alignment, the regression-based CNN is selected as the algorithm to achieve fast convergence. Then, due to the influence of various factors, the extracted feature points are not completely correct, and Gabor features are used to screen the matching of feature points. Finally, by analysing the principle of 3DMM 3D face reconstruction, a single-view 3D face reconstruction method based on CNN is proposed. The experimental results show that the algorithm in this paper has good reconstruction performance and real-time performance and can realize the rapid modelling of human face.

3D Face Reconstruction from Two Orthogonal Images for Face Recognition Applications

2012 ◽  
pp. 223-239
Author(s):  
Stefano Berretti ◽  
Alberto Del Bimbo ◽  
Pietro Pala
Keyword(s):  
Face Recognition ◽  
Control Points ◽  
3D Face Reconstruction ◽  
3D Face ◽  
Face Reconstruction ◽  
Face Images ◽  
Model Control ◽  
3D Geometry ◽  
The Face ◽  
Manual Assistance

In this paper, an original hybrid 2D-3D face recognition approach is proposed using two orthogonal face images, frontal and side views of the face, to reconstruct the complete 3D geometry of the face. This is obtained using a model based solution, in which a 3D template face model is morphed according to the correspondence of a limited set of control points identified on the frontal and side images in addition to the model. Control points identification is driven by an Active Shape Model applied to the frontal image, whereas subsequent manual assistance is required for control points localization on the side view. The reconstructed 3D model is finally matched, using the iso-geodesic regions approach against a gallery of 3D face scans for the purpose of face recognition. Preliminary experimental results are provided on a small database showing the viability of the approach.

A Algorithm of Three-Dimensional Human Face Pose Correction Based on the Normal Vector Alignment

2013 ◽  
Vol 734-737 ◽  
pp. 2855-2858
Author(s):  
De Wei Zhang
Keyword(s):  
Three Dimensional ◽  
Normal Direction ◽  
Normal Vector ◽  
Alignment Algorithm ◽  
Feature Points ◽  
Human Face ◽  
A Algorithm ◽  
Correction Effect ◽  
The Face ◽  
Vector Direction

In this paper, we present an approach of three-dimensional human face pose correction with the normal vector alignment algorithm. We detect three feature points on a human face through calculating discrete Gaussian curvature. Then we calculate the three feature points plane of the normal direction. The face pose is corrected from the normal vector direction. This method is small amount of calculation and wide applicability. The experimental results show that the correction effect is good.

Single View 3D Face Reconstruction

2020 ◽  
pp. 215-227
Author(s):  
Claudio Ferrari ◽  
Stefano Berretti ◽  
Alberto del Bimbo
Keyword(s):  
3D Structure ◽  
Three Dimensional ◽  
3D Face Reconstruction ◽  
3D Face ◽  
Single View ◽  
Face Reconstruction ◽  
Current State ◽  
Rgb Images ◽  
Human Faces ◽  
Rgb Image

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.

scholarly journals 3D Face Reconstruction Techniques: Passive Methods

2019 ◽  
Vol 8 (2) ◽  
pp. 4354-4364
Keyword(s):  
3D Face Reconstruction ◽  
3D Images ◽  
3D Face ◽  
Image Reproduction ◽  
Face Reconstruction ◽  
The Face ◽  
History Of ◽  
Human Pose ◽  
Improved Accuracy ◽  
Pose Identification

In the recent literature, 3D face reconstruction received wide interest and has become one of the significant areas of research. 3D face reconstruction provides in depth details on geometrics, texture and color of the face, which are utilized in different applications. It supports a multitude of applications, ranging from face recognition and surveillance to medical imaging, gaming, animation, and virtual reality. This paper attempts to consolidate the research works that have happened in the history of 3D face reconstruction. Also, we try to classify the existing methods based on the input for the process. The databases used in the recent works are discussed and the performance evaluation of methods on different databases is analyzed. The challenges addressed in recent studies are mainly focused on the faster reconstruction of 3D Images, improved accuracy of reconstructed images, human pose identification, image reproduction with higher resolution. Researchers have also tried to address occlusion related problems. Passive methods, used by different researchers are analyzed and their effects on different parameters are discussed in this work. Finally, possible future areas for improvement in terms of reconstructions are presented for the benefit of researchers.

scholarly journals Detail 3D Face Reconstruction Based on 3DMM and Displacement Map

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Tianping Li ◽  
Hongxin Xu ◽  
Hua Zhang ◽  
Honglin Wan
Keyword(s):  
Texture Features ◽  
Facial Feature ◽  
Reconstruction Method ◽  
3D Face Reconstruction ◽  
Face Surface ◽  
Face Model ◽  
Face Reconstruction ◽  
Adversarial Network ◽  
Face Features ◽  
The Face

How to accurately reconstruct the 3D model human face is a challenge issue in the computer vision. Due to the complexity of face reconstruction and diversity of face features, most existing methods are aimed at reconstructing a smooth face model with ignoring face details. In this paper a novel deep learning-based face reconstruction method is proposed. It contains two modules: initial face reconstruction and face details synthesis. In the initial face reconstruction module, a neural network is used to detect the facial feature points and the angle of the pose face, and 3D Morphable Model (3DMM) is used to reconstruct the rough shape of the face model. In the face detail synthesis module, Conditional Generation Adversarial Network (CGAN) is used to synthesize the displacement map. The map provides texture features to render to the face surface reconstruction, so as to reflect the face details. Our proposal is evaluated by Facescape dataset in experiments and achieved better performance than other current methods.

Multi-pose 3D facial texture refinement for face recognition

2018 ◽  
Vol 16 (02) ◽  
pp. 1840006
Author(s):  
Wanshun Gao ◽  
Xi Zhao ◽  
Jun An ◽  
Jianhua Zou
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Competitive Performance ◽  
3D Face Reconstruction ◽  
3D Face ◽  
Face Reconstruction ◽  
Novel Approach ◽  
Art Methods ◽  
The Face ◽  
Facial Images

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.

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