Predicting Novel Views Using Generative Adversarial Query Network

Phong Nguyen-Ha; Lam Huynh; Esa Rahtu; Janne Heikkila

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arXiv · 1904.05124

Predicting Novel Views Using Generative Adversarial Query Network

Phong Nguyen-Ha·Lam Huynh·Esa Rahtu·Janne Heikkila

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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.

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BibTeX RIS

Phong Nguyen-Ha, Lam Huynh, Esa Rahtu, Janne Heikkila. 2019-04-10. Predicting Novel Views Using Generative Adversarial Query Network. https://arxiv.org/abs/1904.05124

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