  Contemporary benchmark methods for image inpainting are based on deep
generative models and specifically leverage adversarial loss for yielding
realistic reconstructions. However, these models cannot be directly applied on
image/video sequences because of an intrinsic drawback- the reconstructions
might be independently realistic, but, when visualized as a sequence, often
lacks fidelity to the original uncorrupted sequence. The fundamental reason is
that these methods try to find the best matching latent space representation
near to natural image manifold without any explicit distance based loss. In
this paper, we present a semantically conditioned Generative Adversarial
Network (GAN) for sequence inpainting. The conditional information constrains
the GAN to map a latent representation to a point in image manifold respecting
the underlying pose and semantics of the scene. To the best of our knowledge,
this is the first work which simultaneously addresses consistency and
correctness of generative model based inpainting. We show that our generative
model learns to disentangle pose and appearance information; this independence
is exploited by our model to generate highly consistent reconstructions. The
conditional information also aids the generator network in GAN to produce
sharper images compared to the original GAN formulation. This helps in
achieving more appealing inpainting performance. Though generic, our algorithm
was targeted for inpainting on faces. When applied on CelebA and Youtube Faces
datasets, the proposed method results in a significant improvement over the
current benchmark, both in terms of quantitative evaluation (Peak Signal to
Noise Ratio) and human visual scoring over diversified combinations of
resolutions and deformations.
