  Neural fields have emerged as a new data representation paradigm and have
shown remarkable success in various signal representations. Since they preserve
signals in their network parameters, the data transfer by sending and receiving
the entire model parameters prevents this emerging technology from being used
in many practical scenarios. We propose streamable neural fields, a single
model that consists of executable sub-networks of various widths. The proposed
architectural and training techniques enable a single network to be streamable
over time and reconstruct different qualities and parts of signals. For
example, a smaller sub-network produces smooth and low-frequency signals, while
a larger sub-network can represent fine details. Experimental results have
shown the effectiveness of our method in various domains, such as 2D images,
videos, and 3D signed distance functions. Finally, we demonstrate that our
proposed method improves training stability, by exploiting parameter sharing.
