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Novel-view synthesis (NVS) can be tackled through different approaches,depending on the general setting: a single source image to a short videosequence, exact or noisy camera pose information, 3D-based information such aspoint clouds etc. The most challenging scenario, the one where we stand in thiswork, only considers a unique source image to generate a novel one from anotherviewpoint. However, in such a tricky situation, the latest learning-basedsolutions often struggle to integrate the camera viewpoint transformation.Indeed, the extrinsic information is often passed as-is, through alow-dimensional vector. It might even occur that such a camera pose, whenparametrized as Euler angles, is quantized through a one-hot representation.This vanilla encoding choice prevents the learnt architecture from inferringnovel views on a continuous basis (from a camera pose perspective). We claim itexists an elegant way to better encode relative camera pose, by leveraging3D-related concepts such as the epipolar constraint. We, therefore, introducean innovative method that encodes the viewpoint transformation as a 2D featureimage. Such a camera encoding strategy gives meaningful insights to the networkregarding how the camera has moved in space between the two views. By encodingthe camera pose information as a finite number of coloured epipolar lines, wedemonstrate through our experiments that our strategy outperforms vanillaencoding.

EpipolarNVS: leveraging on Epipolar geometry for single-image Novel View Synthesis