  Novel view synthesis often needs the paired data from both the source and
target views. This paper proposes a view translation model under cVAE-GAN
framework without requiring the paired data. We design a conditional deformable
module (CDM) which uses the view condition vectors as the filters to convolve
the feature maps of the main branch in VAE. It generates several pairs of
displacement maps to deform the features, like the 2D optical flows. The
results are fed into the deformed feature based normalization module (DFNM),
which scales and offsets the main branch feature, given its deformed one as the
input from the side branch. Taking the advantage of the CDM and DFNM, the
encoder outputs a view-irrelevant posterior, while the decoder takes the code
drawn from it to synthesize the reconstructed and the viewtranslated images. To
further ensure the disentanglement between the views and other factors, we add
adversarial training on the code. The results and ablation studies on MultiPIE
and 3D chair datasets validate the effectiveness of the framework in cVAE and
the designed module.
