  In this work we propose a deep learning pipeline to predict the visual future
appearance of an urban scene. Despite recent advances, generating the entire
scene in an end-to-end fashion is still far from being achieved. Instead, here
we follow a two stages approach, where interpretable information is included in
the loop and each actor is modelled independently. We leverage a per-object
novel view synthesis paradigm; i.e. generating a synthetic representation of an
object undergoing a geometrical roto-translation in the 3D space. Our model can
be easily conditioned with constraints (e.g. input trajectories) provided by
state-of-the-art tracking methods or by the user itself. This allows us to
generate a set of diverse realistic futures starting from the same input in a
multi-modal fashion. We visually and quantitatively show the superiority of
this approach over traditional end-to-end scene-generation methods on CityFlow,
a challenging real world dataset.
