  We address the problem of novel view video prediction; given a set of input
video clips from a single/multiple views, our network is able to predict the
video from a novel view. The proposed approach does not require any priors and
is able to predict the video from wider angular distances, upto 45 degree, as
compared to the recent studies predicting small variations in viewpoint.
Moreover, our method relies only onRGB frames to learn a dual representation
which is used to generate the video from a novel viewpoint. The dual
representation encompasses a view-dependent and a global representation which
incorporates complementary details to enable novel view video prediction. We
demonstrate the effectiveness of our framework on two real world datasets:
NTU-RGB+D and CMU Panoptic. A comparison with the State-of-the-art novel view
video prediction methods shows an improvement of 26.1% in SSIM, 13.6% in PSNR,
and 60% inFVD scores without using explicit priors from target views.
