  Contemporary deep learning based inpainting algorithms are mainly based on a
hybrid dual stage training policy of supervised reconstruction loss followed by
an unsupervised adversarial critic loss. However, there is a dearth of
literature for a fully unsupervised GAN based inpainting framework. The primary
aversion towards the latter genre is due to its prohibitively slow iterative
optimization requirement during inference to find a matching noise prior for a
masked image. In this paper, we show that priors matter in GAN: we learn a data
driven parametric network to predict a matching prior for a given image. This
converts an iterative paradigm to a single feed forward inference pipeline with
a massive 1500X speedup and simultaneous improvement in reconstruction quality.
We show that an additional structural prior imposed on GAN model results in
higher fidelity outputs. To extend our model for sequence inpainting, we
propose a recurrent net based grouped noise prior learning. To our knowledge,
this is the first demonstration of an unsupervised GAN based sequence
inpainting. A further improvement in sequence inpainting is achieved with an
additional subsequence consistency loss. These contributions improve the
spatio-temporal characteristics of reconstructed sequences. Extensive
experiments conducted on SVHN, Standford Cars, CelebA and CelebA-HQ image
datasets, synthetic sequences and ViDTIMIT video datasets reveal that we
consistently improve upon previous unsupervised baseline and also achieve
comparable performances(sometimes also better) to hybrid benchmarks.
