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Contemporary deep learning based inpainting algorithms are mainly based on ahybrid dual stage training policy of supervised reconstruction loss followed byan unsupervised adversarial critic loss. However, there is a dearth ofliterature for a fully unsupervised GAN based inpainting framework. The primaryaversion towards the latter genre is due to its prohibitively slow iterativeoptimization requirement during inference to find a matching noise prior for amasked image. In this paper, we show that priors matter in GAN: we learn a datadriven parametric network to predict a matching prior for a given image. Thisconverts an iterative paradigm to a single feed forward inference pipeline witha massive 1500X speedup and simultaneous improvement in reconstruction quality.We show that an additional structural prior imposed on GAN model results inhigher fidelity outputs. To extend our model for sequence inpainting, wepropose a recurrent net based grouped noise prior learning. To our knowledge,this is the first demonstration of an unsupervised GAN based sequenceinpainting. A further improvement in sequence inpainting is achieved with anadditional subsequence consistency loss. These contributions improve thespatio-temporal characteristics of reconstructed sequences. Extensiveexperiments conducted on SVHN, Standford Cars, CelebA and CelebA-HQ imagedatasets, synthetic sequences and ViDTIMIT video datasets reveal that weconsistently improve upon previous unsupervised baseline and also achievecomparable performances(sometimes also better) to hybrid benchmarks.

The Angel is in the Priors: Improving GAN based Image and Sequence Inpainting with Better Noise and Structural Priors