Semi-Supervised Learning in Reconstructed Manifold Space for 3D Caricature Generation

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dc.contributor.authorLiu, Junfaen_US
dc.contributor.authorChen, Yiqiangen_US
dc.contributor.authorMiao, Chunyanen_US
dc.contributor.authorXie, Jinjingen_US
dc.contributor.authorLing, Charles X.en_US
dc.contributor.authorGao, Xingyuen_US
dc.contributor.authorGao, Wenen_US
dc.date.accessioned2015-02-23T09:30:03Z
dc.date.available2015-02-23T09:30:03Z
dc.date.issued2009en_US
dc.description.abstractRecently, 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.en_US
dc.description.number8en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume28en_US
dc.identifier.doi10.1111/j.1467-8659.2009.01418.xen_US
dc.identifier.issn1467-8659en_US
dc.identifier.pages2104-2116en_US
dc.identifier.urihttps://doi.org/10.1111/j.1467-8659.2009.01418.xen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltden_US
dc.titleSemi-Supervised Learning in Reconstructed Manifold Space for 3D Caricature Generationen_US
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