Infinite Nature: Perpetual View Generation of Natural Scenes from a Single Image
Andrew Liu, Richard Tucker, Varun Jampani, Ameesh Makadia, Noah, Snavely, Angjoo Kanazawa

TL;DR
This paper presents a hybrid method for long-range view generation from a single image, combining geometry and image synthesis to produce plausible natural scene sequences over extended camera trajectories.
Contribution
It introduces a novel iterative framework that integrates geometry and image synthesis for perpetual view generation from a single image, enabling long-range scene synthesis.
Findings
Outperforms existing methods in generating longer scene sequences
Can be trained on monocular video data
Effective on aerial coastal scene footage
Abstract
We introduce the problem of perpetual view generation - long-range generation of novel views corresponding to an arbitrarily long camera trajectory given a single image. This is a challenging problem that goes far beyond the capabilities of current view synthesis methods, which quickly degenerate when presented with large camera motions. Methods for video generation also have limited ability to produce long sequences and are often agnostic to scene geometry. We take a hybrid approach that integrates both geometry and image synthesis in an iterative `\emph{render}, \emph{refine} and \emph{repeat}' framework, allowing for long-range generation that cover large distances after hundreds of frames. Our approach can be trained from a set of monocular video sequences. We propose a dataset of aerial footage of coastal scenes, and compare our method with recent view synthesis and conditional…
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Taxonomy
TopicsAdvanced Vision and Imaging · Generative Adversarial Networks and Image Synthesis · Computer Graphics and Visualization Techniques
MethodsAdam · 1-bit Adam
