Multi-view to Novel View: Synthesizing Novel Views With Self-learned Confidence
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Journal Volume
11207 LNCS
Pages
162-178
Date Issued
2018
Author(s)
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. © 2018, Springer Nature Switzerland AG.
Subjects
Multi-view novel view synthesis; Novel view synthesis
Other Subjects
Computer vision; Forecasting; Pixels; Aggregation mechanism; Flow prediction; Multi-views; Multiple viewpoints; Novel view synthesis; Source images; State of the art; Target images; Image enhancement
Type
conference paper