NIFTY: A NON-LOCAL IMAGE FLOW MATCHING FOR TEXTURE SYNTHESIS

Pierrick Chatillon; Julien Rabin; David Tschumperlé

ICASSP 2026poster0 citations

NIFTY: A NON-LOCAL IMAGE FLOW MATCHING FOR TEXTURE SYNTHESIS

Pierrick Chatillon, Julien Rabin, David Tschumperlé

Abstract

This paper addresses the problem of exemplar-based texture synthesis. We introduce NIFTY, a hybrid framework that combines recent insights on diffusion models trained with convolutional neural networks, and classical patch-based texture optimization techniques. NIFTY is a non-parametric flow-matching model built on non-local patch matching, which avoids the need for neural network training while alleviating common shortcomings of patch-based methods, such as poor initialization or visual artifacts. Experimental results demonstrate the effectiveness of the proposed approach compared to representative methods from the literature. Code is available at https://github.com/PierrickCh/Nifty.git

BibTeX

@inproceedings{icassp2026_niftyanonlocalim,
  title = {NIFTY: A NON-LOCAL IMAGE FLOW MATCHING FOR TEXTURE SYNTHESIS},
  author = {Pierrick Chatillon and Julien Rabin and David Tschumperlé},
  booktitle = {ICASSP 2026},
  year = {2026}
}