  View synthesis aims to generate novel views from one or more given source
views. Although existing methods have achieved promising performance, they
usually require paired views of different poses to learn a pixel
transformation. This paper proposes an unsupervised network to learn such a
pixel transformation from a single source viewpoint. In particular, the network
consists of a token transformation module (TTM) that facilities the
transformation of the features extracted from a source viewpoint image into an
intrinsic representation with respect to a pre-defined reference pose and a
view generation module (VGM) that synthesizes an arbitrary view from the
representation. The learned transformation allows us to synthesize a novel view
from any single source viewpoint image of unknown pose. Experiments on the
widely used view synthesis datasets have demonstrated that the proposed network
is able to produce comparable results to the state-of-the-art methods despite
the fact that learning is unsupervised and only a single source viewpoint image
is required for generating a novel view. The code will be available soon.
