  We present a neural rendering framework that maps a voxelized scene into a
high quality image. Highly-textured objects and scene element interactions are
realistically rendered by our method, despite having a rough representation as
an input. Moreover, our approach allows controllable rendering: geometric and
appearance modifications in the input are accurately propagated to the output.
The user can move, rotate and scale an object, change its appearance and
texture or modify the position of the light and all these edits are represented
in the final rendering. We demonstrate the effectiveness of our approach by
rendering scenes with varying appearance, from single color per object to
complex, high-frequency textures. We show that our rerendering network can
generate very detailed images that represent precisely the appearance of the
input scene. Our experiments illustrate that our approach achieves more
accurate image synthesis results compared to alternatives and can also handle
low voxel grid resolutions. Finally, we show how our neural rendering framework
can capture and faithfully render objects from real images and from a diverse
set of classes.
