  Novel-view synthesis (NVS) can be tackled through different approaches,
depending on the general setting: a single source image to a short video
sequence, exact or noisy camera pose information, 3D-based information such as
point clouds etc. The most challenging scenario, the one where we stand in this
work, only considers a unique source image to generate a novel one from another
viewpoint. However, in such a tricky situation, the latest learning-based
solutions often struggle to integrate the camera viewpoint transformation.
Indeed, the extrinsic information is often passed as-is, through a
low-dimensional vector. It might even occur that such a camera pose, when
parametrized as Euler angles, is quantized through a one-hot representation.
This vanilla encoding choice prevents the learnt architecture from inferring
novel views on a continuous basis (from a camera pose perspective). We claim it
exists an elegant way to better encode relative camera pose, by leveraging
3D-related concepts such as the epipolar constraint. We, therefore, introduce
an innovative method that encodes the viewpoint transformation as a 2D feature
image. Such a camera encoding strategy gives meaningful insights to the network
regarding how the camera has moved in space between the two views. By encoding
the camera pose information as a finite number of coloured epipolar lines, we
demonstrate through our experiments that our strategy outperforms vanilla
encoding.
