Recently, 3D caricature generation and applications have attracted wide attention from both the research community and the entertainment industry. This paper proposes a novel interactive approach for various and interesting 3D caricature generation based on double sampling. Firstly, according to user's operation, we obtain a coarse 3D caricature with local features transformation by sampling in well-built principle component analysis (PCA) subspace. Secondly, to utilize information of the 2D caricature dataset, we sample in the local linear embedding (LLE) manifold subspace. Finally, we use the learned 2D caricature information to further refine the coarse caricature by applying Kriging interpolation. The experiments show that the 3D caricature generated by our method can preserve highly artistic styles and also reflect the user's intention.

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.