# Predicting Novel Views Using Generative Adversarial Query Network

## TL;DR

The paper presents GAQN, a novel framework combining GQN and GANs to improve the quality and speed of novel view synthesis from multiple observations.

## Contribution

It introduces an adversarial and feature-matching loss to enhance GQN, resulting in higher quality images and faster training convergence.

## Key findings

- GAQN produces higher quality novel views.
- GAQN converges faster than traditional GQN.
- Enhanced visual realism in generated images.

## Abstract

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.