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2018 | OriginalPaper | Buchkapitel

Multi-view to Novel View: Synthesizing Novel Views With Self-learned Confidence

verfasst von : Shao-Hua Sun, Minyoung Huh, Yuan-Hong Liao, Ning Zhang, Joseph J. Lim

Erschienen in: Computer Vision – ECCV 2018

Verlag: Springer International Publishing

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Abstract

In this paper, we address the task of multi-view novel view synthesis, where we are interested in synthesizing a target image with an arbitrary camera pose from given source images. We propose an end-to-end trainable framework that learns to exploit multiple viewpoints to synthesize a novel view without any 3D supervision. Specifically, our model consists of a flow prediction module and a pixel generation module to directly leverage information presented in source views as well as hallucinate missing pixels from statistical priors. To merge the predictions produced by the two modules given multi-view source images, we introduce a self-learned confidence aggregation mechanism. We evaluate our model on images rendered from 3D object models as well as real and synthesized scenes. We demonstrate that our model is able to achieve state-of-the-art results as well as progressively improve its predictions when more source images are available.

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Vorheriges Kapitel Memory Aware Synapses: Learning What (not) to Forget

Nächstes Kapitel Multimodal Unsupervised Image-to-Image Translation

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Titel: Multi-view to Novel View: Synthesizing Novel Views With Self-learned Confidence
verfasst von: Shao-Hua Sun
Minyoung Huh
Yuan-Hong Liao
Ning Zhang
Joseph J. Lim
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2018
Print ISBN: 978-3-030-01218-2

Electronic ISBN: 978-3-030-01219-9

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-01219-9_10

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