  Multi-view frame reconstruction is an important problem particularly when
multiple frames are missing and past and future frames within the camera are
far apart from the missing ones. Realistic coherent frames can still be
reconstructed using corresponding frames from other overlapping cameras. We
propose an adversarial approach to learn the spatio-temporal representation of
the missing frame using conditional Generative Adversarial Network (cGAN). The
conditional input to each cGAN is the preceding or following frames within the
camera or the corresponding frames in other overlapping cameras, all of which
are merged together using a weighted average. Representations learned from
frames within the camera are given more weight compared to the ones learned
from other cameras when they are close to the missing frames and vice versa.
Experiments on two challenging datasets demonstrate that our framework produces
comparable results with the state-of-the-art reconstruction method in a single
camera and achieves promising performance in multi-camera scenario.
