  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
views. Our approach is not limited to generating a single image at a time and
can condition on a variable number of 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 our 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
views that have no natural sequential ordering, like loops and binocular
trajectories, and significantly outperforms other methods on such tasks.
