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

• URL: http://arxiv.org/abs/2509.22318v1
• Date: Fri, 26 Sep 2025 13:19:26 GMT
• Title: NIFTY: a Non-Local Image Flow Matching for Texture Synthesis
• Authors: Pierrick Chatillon, Julien Rabin, David Tschumperlé,
• Abstract summary: NIFTY is a hybrid framework that combines recent insights on diffusion models trained with convolutional neural networks, and patch-based texture optimization techniques.<n>NIFTY is a non-parametric flow-matching model built on non-local patch matching, which avoids the need for neural network training.
• Score: 0.688204255655161
• License: http://creativecommons.org/licenses/by/4.0/
• 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

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