PolyOculus: Simultaneous Multi-view Image-based Novel View Synthesis
Jason J. Yu Tristan Aumentado-Armstrong Fereshteh Forghani Konstantinos G. Derpanis Marcus A. Brubaker
This paper considers the problem of generative novel view synthesis (GNVS),
generating novel, plausible views of a scene given a limited number of known
views. Here, we propose a set-based generative model that can simultaneously
generate multiple, self-consistent new views, conditioned on any number of
known views. Our approach is not limited to generating a single image at a time
and can condition on zero, one, or more views. As a result, when generating a
large number of views, our method is not restricted to a low-order
autoregressive generation approach and is better able to maintain generated
image quality over large sets of images. We evaluate the proposed model on
standard NVS datasets and show that it outperforms the state-of-the-art
image-based GNVS baselines. Further, we show that the model is capable of
generating sets of camera views that have no natural sequential ordering, like
loops and binocular trajectories, and significantly outperforms other methods
on such tasks.