NIFTY: A Non-Local Image Flow Matching for Texture Synthesis
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.
Fields of Study & Subjects
Generative Adversarial Networks and Image Synthesis
Medical Image Segmentation Techniques
Image Retrieval and Classification Techniques
Publication details
- Journal / source
- ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Pages
- 11142–11146
- Publication date
- 21 April 2026
- Language
- EN