  Neural representations are popular for representing shapes, as they can be
learned form sensor data and used for data cleanup, model completion, shape
editing, and shape synthesis. Current neural representations can be categorized
as either overfitting to a single object instance, or representing a collection
of objects. However, neither allows accurate editing of neural scene
representations: on the one hand, methods that overfit objects achieve highly
accurate reconstructions, but do not generalize to unseen object configurations
and thus cannot support editing; on the other hand, methods that represent a
family of objects with variations do generalize but produce only approximate
reconstructions. We propose NEUFORM to combine the advantages of both
overfitted and generalizable representations by adaptively using the one most
appropriate for each shape region: the overfitted representation where reliable
data is available, and the generalizable representation everywhere else. We
achieve this with a carefully designed architecture and an approach that blends
the network weights of the two representations, avoiding seams and other
artifacts. We demonstrate edits that successfully reconfigure parts of
human-designed shapes, such as chairs, tables, and lamps, while preserving
semantic integrity and the accuracy of an overfitted shape representation. We
compare with two state-of-the-art competitors and demonstrate clear
improvements in terms of plausibility and fidelity of the resultant edits.
