  Despite recent advances in implicit neural representations (INRs), it remains
challenging for a coordinate-based multi-layer perceptron (MLP) of INRs to
learn a common representation across data instances and generalize it for
unseen instances. In this work, we introduce a simple yet effective framework
for generalizable INRs that enables a coordinate-based MLP to represent complex
data instances by modulating only a small set of weights in an early MLP layer
as an instance pattern composer; the remaining MLP weights learn pattern
composition rules for common representations across instances. Our
generalizable INR framework is fully compatible with existing meta-learning and
hypernetworks in learning to predict the modulated weight for unseen instances.
Extensive experiments demonstrate that our method achieves high performance on
a wide range of domains such as an audio, image, and 3D object, while the
ablation study validates our weight modulation.
