  In this paper, we propose to improve the inference speed and visual quality
of contemporary baseline of Generative Adversarial Networks (GAN) based
unsupervised semantic inpainting. This is made possible with better
initialization of the core iterative optimization involved in the framework. To
our best knowledge, this is also the first attempt of GAN based video
inpainting with consideration to temporal cues. On single image inpainting, we
achieve about 4.5-5$\times$ speedup and 80$\times$ on videos compared to
baseline. Simultaneously, our method has better spatial and temporal
reconstruction qualities as found on three image and one video dataset.
