  We present a new method for lightweight novel-view synthesis that generalizes
to an arbitrary forward-facing scene. Recent approaches are computationally
expensive, require per-scene optimization, or produce a memory-expensive
representation. We start by representing the scene with a set of
fronto-parallel semitransparent planes and afterward convert them to deformable
layers in an end-to-end manner. Additionally, we employ a feed-forward
refinement procedure that corrects the estimated representation by aggregating
information from input views. Our method does not require fine-tuning when a
new scene is processed and can handle an arbitrary number of views without
restrictions. Experimental results show that our approach surpasses recent
models in terms of common metrics and human evaluation, with the noticeable
advantage in inference speed and compactness of the inferred layered geometry,
see https://samsunglabs.github.io/MLI
