Paper
31 January 2020 View synthesis by shared conditional adversarial autoencoder
Xingya Chang, Dongyue Chen, Qiusheng Chen, Tong Jia, Hongyu Wang
Author Affiliations +
Proceedings Volume 11427, Second Target Recognition and Artificial Intelligence Summit Forum; 114270T (2020) https://doi.org/10.1117/12.2550551
Event: Second Target Recognition and Artificial Intelligence Summit Forum, 2019, Changchun, China
Abstract
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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Xingya Chang, Dongyue Chen, Qiusheng Chen, Tong Jia, and Hongyu Wang "View synthesis by shared conditional adversarial autoencoder", Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114270T (31 January 2020); https://doi.org/10.1117/12.2550551
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KEYWORDS
3D modeling

Data modeling

Network architectures

Gallium nitride

Image quality

RGB color model

3D image processing

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