KEYWORDS: 3D modeling, Data modeling, Network architectures, Gallium nitride, Image quality, RGB color model, 3D image processing, Visual process modeling, 3D displays, Optical flow
An important problem for both image processing and computer version is to synthesize the novel view of a 3D object. We propose a shared conditional adversarial auto-encoder (SCAAE) network that is trained end-to-end on the task of rendering previously unseen object given a single image of this object. The model uses the advanced GAN framework to build the generator by introducing U-net, which can generate a novel view image based on the input image and a controllable condition signal. The FCN model is used to construct the D-network to distinguish real and fake images. We also propose a new objective function which considers both the distribution consistency and transformation persistence. We designed a SCAEE network to generate multi-view images of objects, instead of the three dimensional effect of physical models, which solves the shortcoming of artificial modeling. Experiments demonstrate that the new network structure is better than other already existing.
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