  In this paper, we firstly consider view-dependent effects into single
image-based novel view synthesis (NVS) problems. For this, we propose to
exploit the camera motion priors in NVS to model view-dependent appearance or
effects (VDE) as the negative disparity in the scene. By recognizing
specularities "follow" the camera motion, we infuse VDEs into the input images
by aggregating input pixel colors along the negative depth region of the
epipolar lines. Also, we propose a `relaxed volumetric rendering' approximation
that allows computing the densities in a single pass, improving efficiency for
NVS from single images. Our method can learn single-image NVS from image
sequences only, which is a completely self-supervised learning method, for the
first time requiring neither depth nor camera pose annotations. We present
extensive experiment results and show that our proposed method can learn NVS
with VDEs, outperforming the SOTA single-view NVS methods on the RealEstate10k
and MannequinChallenge datasets.
