An Image Representation for the 3D Face Synthesis

Author(s):  
Guoliang Luo ◽  
Wei Zeng ◽  
Wenqiang Xie ◽  
Haopeng Lei ◽  
Chuhua Xian
Author(s):  
Yining Lang ◽  
Wei Liang ◽  
Yujia Wang ◽  
Lap-Fai Yu

Synthesizing 3D faces that give certain personality impressions is commonly needed in computer games, animations, and virtual world applications for producing realistic virtual characters. In this paper, we propose a novel approach to synthesize 3D faces based on personality impression for creating virtual characters. Our approach consists of two major steps. In the first step, we train classifiers using deep convolutional neural networks on a dataset of images with personality impression annotations, which are capable of predicting the personality impression of a face. In the second step, given a 3D face and a desired personality impression type as user inputs, our approach optimizes the facial details against the trained classifiers, so as to synthesize a face which gives the desired personality impression. We demonstrate our approach for synthesizing 3D faces giving desired personality impressions on a variety of 3D face models. Perceptual studies show that the perceived personality impressions of the synthesized faces agree with the target personality impressions specified for synthesizing the faces.


2006 ◽  
Vol 86 (10) ◽  
pp. 2932-2951 ◽  
Author(s):  
Arman Savran ◽  
Levent M. Arslan ◽  
Lale Akarun

2014 ◽  
pp. 42-49
Author(s):  
Agata Manolova ◽  
Krasimir Tonchev

In this paper we present a comparative analysis of two algorithms for image representation with application to recognition of 3D face scans with the presence of facial expressions. We begin with processing of the input point cloud based on curvature analysis and range image representation to achieve a unique representation of the face features. Then, subspace projection using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) is performed. Finally classification with different classifiers will be performed over the 3D face scans dataset with 61 subject with 7 scans per subject (427 scans), namely two "frontal", one "look-up", one "look-down", one "smile", one "laugh", one "random expression". The experimental results show a high recognition rate for the chosen database. They demonstrate the effectiveness of the proposed 3D image representations and subspace projection for 3D face recognition.


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