  We extend neural 3D representations to allow for intuitive and interpretable
user control beyond novel view rendering (i.e. camera control). We allow the
user to annotate which part of the scene one wishes to control with just a
small number of mask annotations in the training images. Our key idea is to
treat the attributes as latent variables that are regressed by the neural
network given the scene encoding. This leads to a few-shot learning framework,
where attributes are discovered automatically by the framework, when
annotations are not provided. We apply our method to various scenes with
different types of controllable attributes (e.g. expression control on human
faces, or state control in movement of inanimate objects). Overall, we
demonstrate, to the best of our knowledge, for the first time novel view and
novel attribute re-rendering of scenes from a single video.
