  Recent implicit neural rendering methods have demonstrated that it is
possible to learn accurate view synthesis for complex scenes by predicting
their volumetric density and color supervised solely by a set of RGB images.
However, existing methods are restricted to learning efficient representations
of static scenes that encode all scene objects into a single neural network,
and lack the ability to represent dynamic scenes and decompositions into
individual scene objects. In this work, we present the first neural rendering
method that decomposes dynamic scenes into scene graphs. We propose a learned
scene graph representation, which encodes object transformation and radiance,
to efficiently render novel arrangements and views of the scene. To this end,
we learn implicitly encoded scenes, combined with a jointly learned latent
representation to describe objects with a single implicit function. We assess
the proposed method on synthetic and real automotive data, validating that our
approach learns dynamic scenes -- only by observing a video of this scene --
and allows for rendering novel photo-realistic views of novel scene
compositions with unseen sets of objects at unseen poses.
