  Neural fields model signals by mapping coordinate inputs to sampled values.
They are becoming an increasingly important backbone architecture across many
fields from vision and graphics to biology and astronomy. In this paper, we
explore the differences between common conditioning mechanisms within these
networks, an essential ingredient in shifting neural fields from memorization
of signals to generalization, where the set of signals lying on a manifold is
modelled jointly. In particular, we are interested in the scaling behaviour of
these mechanisms to increasingly high-dimensional conditioning variables. As we
show in our experiments, high-dimensional conditioning is key to modelling
complex data distributions, thus it is important to determine what architecture
choices best enable this when working on such problems. To this end, we run
experiments modelling 2D, 3D, and 4D signals with neural fields, employing
concatenation, hyper-network, and attention-based conditioning strategies -- a
necessary but laborious effort that has not been performed in the literature.
We find that attention-based conditioning outperforms other approaches in a
variety of settings.
