Work

NIFTY: A Non-Local Image Flow Matching for Texture Synthesis

Year: 2026

Type: conference-paper

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 nonlocal 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.

Source: IEEE International Conference on Acoustics Speech and Signal Processing

DOI: 10.1109/icassp55912.2026.11461935

Authors: Pierrick Chatillon, Julien Rabin, David Tschumperlé

Institutions: Centre National de la Recherche Scientifique, École Nationale Supérieure d'Ingénieurs de Caen, GREYC, Université de Caen Normandie

Language: English

Topic: Generative Adversarial Networks and Image Synthesis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4414934356

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