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Semi‐Supervised Learning in Reconstructed Manifold Space for 3D Caricature Generation
Author(s) -
Liu Junfa,
Chen Yiqiang,
Miao Chunyan,
Xie Jinjing,
Ling Charles X.,
Gao Xingyu,
Gao Wen
Publication year - 2009
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/j.1467-8659.2009.01418.x
Subject(s) - computer science , artificial intelligence , regularization (linguistics) , principal component analysis , set (abstract data type) , manifold (fluid mechanics) , 3d model , machine learning , pattern recognition (psychology) , computer vision , programming language , mechanical engineering , engineering
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.