Efficient car shape design is a challenging problem in both the au-tomotive industry and the computer animation/games industry. Inthis paper, we present a system to reconstruct the 3D car shapefrom a single 2D sketch image 더위치. To learn the correlation between 2Dsketches and 3D cars, we propose a Variational Autoencoder deepneural network that takes a 2D sketch and generates a set of multi-view depth & mask images, which are more effective representationcomparing to 3D mesh, and can be combined to form the 3D carshape 한글 2014 vp. To ensure the volume and diversity of the training data,we propose a feature-preserving car mesh augmentation pipelinefor data augmentation. Since deep learning has limited capacityto reconstruct fine-detail features, we propose a lazy learning ap-proach that constructs a small subspace based on a few relevant carsamples in the database Download a movie from Ain. Due to the small size of such a subspace,fine details can be represented effectively with a small number ofparameters. With a low-cost optimization process, a high-qualitycar with detailed features is created 트위치. Experimental results showthat the system performs consistently to create highly realisticcars of substantially different shape and topology, with a very lowcomputational cost 워킹데드 시즌8.