Polynomial Neural Fields for Subband Decomposition and Manipulation

Guandao Yang · Sagie Benaim · Varun Jampani · Kyle Genova · Jonathan Barron · Thomas Funkhouser · Bharath Hariharan · Serge Belongie

Hall J #904

Keywords: [ Signal Manipulation ] [ Subband Decomposition ] [ Signal Processing ] [ neural fields ]

[ Abstract ]
[ Paper [ Poster [ OpenReview
Tue 29 Nov 2 p.m. PST — 4 p.m. PST


Neural fields have emerged as a new paradigm for representing signals, thanks to their ability to do it compactly while being easy to optimize. In most applications, however, neural fields are treated like a black box, which precludes many signal manipulation tasks. In this paper, we propose a new class of neural fields called basis-encoded polynomial neural fields (PNFs). The key advantage of a PNF is that it can represent a signal as a composition of a number of manipulable and interpretable components without losing the merits of neural fields representation. We develop a general theoretical framework to analyze and design PNFs. We use this framework to design Fourier PNFs, which match state-of-the-art performance in signal representation tasks that use neural fields. In addition, we empirically demonstrate that Fourier PNFs enable signal manipulation applications such as texture transfer and scale-space interpolation. Code is available at

Chat is not available.