  While frame-independent predictions with deep neural networks have become the
prominent solutions to many computer vision tasks, the potential benefits of
utilizing correlations between frames have received less attention. Even though
probabilistic machine learning provides the ability to encode correlation as
prior knowledge for inference, there is a tangible gap between the theory and
practice of applying probabilistic methods to modern vision problems. For this,
we derive a principled framework to combine information coupling between camera
poses (translation and orientation) with deep models. We proposed a novel view
kernel that generalizes the standard periodic kernel in $\mathrm{SO}(3)$. We
show how this soft-prior knowledge can aid several pose-related vision tasks
like novel view synthesis and predict arbitrary points in the latent space of
generative models, pointing towards a range of new applications for inter-frame
reasoning.
