Automatic 3D Face Feature Points Extraction with Spin Images

Author(s):  
Cristina Conde ◽  
Licesio J. Rodríguez-Aragón ◽  
Enrique Cabello
Keyword(s):  
3D Face ◽  
2010 ◽  
Vol 20 (3) ◽  
pp. 724-733 ◽  
Author(s):  
Xun GONG ◽  
Guo-Yin WANG

Author(s):  
Wang Yushun ◽  
Zhuang Yueting

Online interaction with 3D facial animation is an alternative way of face-to-face communication for distance education. 3D facial modeling is essential for virtual educational environments establishment. This article presents a novel 3D facial modeling solution that facilitates quasi-facial communication for online learning. Our algorithm builds 3D facial models from a single image, with support of a 3D face database. First from the image, we extract a set of feature points, which are then used to automatically estimate the head pose parameters using the 3D mean face in our database as a reference model. After the pose recovery, a similarity measurement function is proposed to locate the neighborhood for the given image in the 3D face database. The scope of neighborhood can be determined adaptively using our cross-validation algorithm. Furthermore, the individual 3D shape is synthesized by neighborhood interpolation. Texture mapping is achieved based on feature points. The experimental results show that our algorithm can robustly produce 3D facial models from images captured in various scenarios to enhance the lifelikeness in distant learning.


Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Limin Xu

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.


2010 ◽  
pp. 727-737
Author(s):  
Yushun Wang ◽  
Yueting Zhuang

Online interaction with 3D facial animation is an alternative way of face-to-face communication for distance education. 3D facial modeling is essential for virtual educational environments establishment. This article presents a novel 3D facial modeling solution that facilitates quasi-facialcommunication for online learning. Our algorithm builds 3D facial models from a single image, with support of a 3D face database. First from the image, we extract a set of feature points, which are then used to automatically estimate the head pose parameters using the 3D mean face in our database as a reference model. After the pose recovery, a similarity measurement function is proposed to locate the neighborhood for the given image in the 3D face database. The scope of neighborhood can be determined adaptivelyusing our cross-validation algorithm. Furthermore, the individual 3D shape is synthesized by neighborhood interpolation. Texture mapping is achieved based on feature points. The experimental results show that our algorithm can robustly produce 3D facial models from images captured in various scenarios to enhance the lifelikeness in distant learning.


Author(s):  
Varin CHOUVATUT ◽  
Suthep MADARASMI ◽  
Mihran TUCERYAN
Keyword(s):  

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