Polynomial Neural Fields for Subband Decomposition and Manipulation

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  • Guandao Yang
  • Sagie Benaim
  • Varun Jampani
  • Kyle Genova
  • Jonathan T. Barron
  • Thomas Funkhouser
  • Bharath Hariharan
  • Belongie, Serge

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 black boxes, which precludes many signal manipulation tasks. In this paper, we propose a new class of neural fields called 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 https://github.com/stevenygd/PNF.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022
EditorsS. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, A. Oh
PublisherNeural Information Processing Systems Foundation
Publication date2022
ISBN (Electronic)9781713871088
Publication statusPublished - 2022
Event36th Conference on Neural Information Processing Systems, NeurIPS 2022 - New Orleans, United States
Duration: 28 Nov 20229 Dec 2022

Conference

Conference36th Conference on Neural Information Processing Systems, NeurIPS 2022
LandUnited States
ByNew Orleans
Periode28/11/202209/12/2022
SeriesAdvances in Neural Information Processing Systems
Volume35
ISSN1049-5258

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