  We propose a novel controller synthesis involving feedback from pixels,
whereby the measurement is a high dimensional signal representing a pixelated
image with Red-Green-Blue (RGB) values. The approach neither requires feature
extraction, nor object detection, nor visual correspondence. The control policy
does not involve the estimation of states or similar latent representations.
Instead, tracking is achieved directly in image space, with a model of the
reference signal embedded as required by the internal model principle. The
reference signal is generated by a neural network with learning-based scene
view synthesis capabilities. Our approach does not require an end-to-end
learning of a pixel-to-action control policy. The approach is applied to a
motion control problem, namely the longitudinal dynamics of a car-following
problem. We show how this approach lend itself to a tractable stability
analysis with associated bounds critical to establishing trustworthiness and
interpretability of the closed-loop dynamics.
