mlsanityData to 14 Oct 2026

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

arXiv 2509.22318 ↗First posted 26 Sep 2025cs.CV cs.LGcited by 1

On the map: Diffusion models → 3D, point cloud and scene generation

Abstract (arXiv)

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

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models. Builds on 14 of its 27 references that are in the map. First posted on arXiv 26 Sep 2025.

Builds on

14 of 27 references.

References · 14

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  3. 2020Denoising Diffusion Implicit Modelscited by 9,781
  4. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  5. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  6. 2022Flow Matching for Generative Modelingcited by 5,702
  7. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
  8. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,700
  9. 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 3,045
  10. 2022On Distillation of Guided Diffusion Modelscited by 725
  11. 2019SinGAN: Learning a Generative Model from a Single Natural Imagecited by 348
  12. 2024An analytic theory of creativity in convolutional diffusion modelscited by 104
2 more
  1. 2021Drop the GAN: In Defense of Patches Nearest Neighbors as Single Image Generative Modelscited by 51
  2. 2024Generating Non-Stationary Textures using Self-Rectificationcited by 8

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Efficient and Training-Free Single-Image Diffusion Modelscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024Infinite Texture: Text-guided High Resolution Diffusion Texture Synthesis
  2. 2023Diffusion-based Holistic Texture Rectification and Synthesis
  3. 2023Single Mesh Diffusion Models with Field Latents for Texture Generation
  4. 2023Sin3DM: Learning a Diffusion Model from a Single 3D Textured Shape
  5. 2024On Synthetic Texture Datasets: Challenges, Creation, and Curation
  6. 2023Flow Matching in Latent Space
  7. 2024RoCoTex: A Robust Method for Consistent Texture Synthesis with Diffusion Models
  8. 2023Boosting Latent Diffusion with Flow Matching