  The problem of predicting a novel view of the scene using an arbitrary number
of observations is a challenging problem for computers as well as for humans.
This paper introduces the Generative Adversarial Query Network (GAQN), a
general learning framework for novel view synthesis that combines Generative
Query Network (GQN) and Generative Adversarial Networks (GANs). The
conventional GQN encodes input views into a latent representation that is used
to generate a new view through a recurrent variational decoder. The proposed
GAQN builds on this work by adding two novel aspects: First, we extend the
current GQN architecture with an adversarial loss function for improving the
visual quality and convergence speed. Second, we introduce a feature-matching
loss function for stabilizing the training procedure. The experiments
demonstrate that GAQN is able to produce high-quality results and faster
convergence compared to the conventional approach.
