![]() To this end, our method decomposes the deformations in each space into two parts: an identity-independent part that is represented with the deformation gradient of the blendshape template, and an identity-dependent part that is modeled by a low-rank linear model. ![]() Given a pre-defined blendshape template of the real facial expressions and corresponding cartoonized blendshape template created by an artist, we represent the blendshapes of an identity in the real and cartoon face spaces with the deformations of the blendshape template in each space and learn a mapping between the deformations in the two spaces. We present a data-driven method for automatically constructing cartoonized 3D blendshapes of a subject’s face. Moreover, our system has been applied successfully in a massive multi-user educational game to provide human-like avatars. The experiments show that our novel approach synthesizes the 3D caricature more effectively than traditional methods. We then predict 3D caricatures for 2D real faces with the learnt model. ![]() 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 reconstruct 3D caricatures based on some 2D caricature samples with a Principal Component Analysis (PCA)-based method. First, the training set is enlarged by reconstructing 3D caricatures. This paper addresses this problem by two steps. However, the lack of 3D caricature samples makes it challenging to train a good model. Machine learning has been proven effective in the automatic generation of caricatures. Abstract Recently, automatic 3D caricature generation has attracted much attention from both the research community and the game industry. ![]()
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