Semi-Supervised Learning in Reconstructed Manifold Space for 3D Caricature Generation
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Date
2009
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and Blackwell Publishing Ltd
Abstract
Recently, automatic 3D caricature generation has attracted much attention from both the research community and the game industry. Machine learning has been proven effective in the automatic generation of caricatures. However, the lack of 3D caricature samples makes it challenging to train a good model. This paper addresses this problem by two steps. First, the training set is enlarged by reconstructing 3D caricatures. We reconstruct 3D caricatures based on some 2D caricature samples with a Principal Component Analysis (PCA)-based method. Secondly, between the 2D real faces and the enlarged 3D caricatures, a regressive model is learnt by the semi-supervised manifold regularization (MR) method. We then predict 3D caricatures for 2D real faces with the learnt model. The experiments show that our novel approach synthesizes the 3D caricature more effectively than traditional methods. Moreover, our system has been applied successfully in a massive multi-user educational game to provide human-like avatars.
Description
@article{10.1111:j.1467-8659.2009.01418.x,
journal = {Computer Graphics Forum},
title = {{Semi-Supervised Learning in Reconstructed Manifold Space for 3D Caricature Generation}},
author = {Liu, Junfa and Chen, Yiqiang and Miao, Chunyan and Xie, Jinjing and Ling, Charles X. and Gao, Xingyu and Gao, Wen},
year = {2009},
publisher = {The Eurographics Association and Blackwell Publishing Ltd},
ISSN = {1467-8659},
DOI = {10.1111/j.1467-8659.2009.01418.x}
}